Vide Item No 613 R Revised syllabus of 1 MSc IT specialized in_1 Syllabus Mumbai University

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AC – 11/07/2022
ItemNo. 6.13 ( 1) (R)







































UNIVERSITY OF MUMBAI



Revised Syllabus for
M.Sc . IT
(Artificial Intelligence)

PartII (Semester I to IV)
(Choice Based Credit System )




(With effect from the academic year 2022 -2023)

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Semester –I
Course Code Course Title Credits
PSIT101 Researchin Computing 4
PSIT102 Data Science 4
PSIT103 Cloud Computing 4
PSIT104 SoftComputing Techniques 4
PSIT1P1 ResearchinComputing Practical 2
PSIT1P2 DataScience Practical 2
PSIT1P3 CloudComputing Practical 2
PSIT1P4 SoftComputingTechniques Practical 2
Total Credits 24


Semester –II
Course Code Course Title Credits
PSIT201 BigData Analytics 4
PSIT202 Modern Networking 4
PSIT203 Microservices Architecture 4
PSIT204 Image Processing 4
PSIT2P1 BigDataAnalytics Practical 2
PSIT2P2 ModernNetworking Practical 2
PSIT2P3 MicroservicesArchitecture Practical 2
PSIT2P4 ImageProcessing Practical 2
Total Credits 24

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ProgramSpecific Outcomes
PSO1: Ability to applythe knowledge ofInformation Technology with recent trendsalignedwith
research and industry.

PSO2: Ability to apply IT in the field of Computational Research, Soft Computing, Big Data
Analytics, Data Science, Image Processing, Artificial Intelligence, Networking and Cloud
Computing.

PSO3: Ability to provide socially acceptable technical solutions in the domains of Information
Security,MachineLearning,InternetofThingsandEmbeddedSystem,InfrastructureServicesas
specializations.

PSO4: Ability to apply the knowledge of Intellectual Property Rights, Cyber Laws and Cyber
Forensics and various standards in interest of National Security and Integrity along with IT
Industry.

PSO5: Ability to write effective project reports, research publications and content development
and to work in multidisciplinary environment in the context of changing technologies.

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SEMESTER I

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 Tobeabletoconductbusinessresearchwithanunderstandingofall the
latest theories.
 Todeveloptheabilitytoexploreresearchtechniquesusedforsolving any
real world or innovate problem. Objectives
Basicknowledgeof statisticalmethods.Analyticalandlogical thinking. Pre requisites M.Sc(Information Technology) Semester – I
CourseName: Researchin Computing CourseCode: PSIT101
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40





Unit Details Lectures
I Introduction: Role of Business Research, Information Systems and
Knowledge Management, Theory Building, Organization ethics and
Issues
12
II Beginning Stages of Research Process: Problem definition,
Qualitative research tools, Secondary data research 12
III Research Methods and Data Collection: Survey research,
communicatingwithrespondents,Observationmethods, Experimental
research
12
IV Measurement Concepts, Samplingand Field work: Levelsof Scale
measurement, attitude measurement, questionnaire design, sampling
designs and procedures, determination of sample size
12
V Data Analysis and Presentation: Editing and Coding, Basic Data
Analysis, Univariate Statistical Analysis and Bivariate Statistical
analysis and differences between two variables. Multivariate Statistical
Analysis.
12


Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. BusinessResearch Methods William
G.Zikmund, B.J
Babin,J.C. Carr, Cengage 8e 2016

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Alearnerwillbeable to:
solve real world problems with scientific approach.
developanalyticalskillsbyapplyingscientificmethods.
recognize,understandandapplythelanguage,theoryandmodelsof the
field of business analytics
fosteranabilityto criticallyanalyze,synthesizeandsolvecomplex
unstructured business problems
understandandcriticallyapplytheconceptsandmethodsof business
analytics
identify,modelandsolvedecisionproblemsindifferentsettings
interpret results/solutions and identify appropriate courses of
action for a given managerial situation whether a problem or an
opportunity
createviablesolutionstodecisionmaking problems
Course Outcome AtanuAdhikari,
M.Griffin
2. Business
Analytics Albright
Winston Cengage 5e 2015
3. ResearchMethods for
BusinessStudentsFifth
Edition Mark Saunders 2011
4. MultivariateData Analysis Hair Pearson 7e 2014

M.Sc(Information Technology) Semester – I
CourseName: ResearchinComputing Practical CourseCode: PSIT1P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hou
rs Marks
Evaluation System Practical Examination 2 40



Practical No Details
1 - 10 10Practicalbasedon abovesyllabus,covering entire syllabus

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Developindepthunderstandingofthekeytechnologiesindatascience and
business analytics: data mining, machine learning, visualization
techniques, predictive modeling, and statistics.
Practiceproblemanalysisanddecision -making.
Gain practical, hands -on experience with statistics programming
languages andbig data tools
throughcourseworkandappliedresearch experiences. Objectives
Basicunderstandingof statistics Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Data Science CourseCode: PSIT102
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Data Science Technology Stack: Rapid Information Factory
Ecosystem, Data Science Storage Tools, Data Lake, Data Vault, Data
WarehouseBusMatrix,DataScience ProcessingTools,Spark,Mesos,
Akka,Cassandra,Kafka,ElasticSearch,R,Scala,Python,MQTT,The
Future
Layered Framework: Definition of Data Science Framework, Cross -
Industry Standard Process for Data Mining (CRISP -DM),
Homogeneous Ontology for Recursive Uniform Schema, The Top
Layers of a Layered Framework, Layered Framework for High -Level
Data Science and Engineering
Business Layer: Business Layer, Engineering a Practical Business
Layer
Utility Layer: Basic Utility Design, Engineering a Practical Utility
Layer




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II Three Management Layers: Operational Management Layer,
Processing -Stream Definition and Management, Audit, Balance, and
Control Layer, Balance, Control, Yoke Solution, Cause -and-Effect,
Analysis System, Functional Layer, Data Science Process
Retrieve Superstep : Data Lakes, Data Swamps, Training the Trainer
Model, Understanding the Business Dynamics of the Data Lake,
Actionable Business Knowledge from Data Lakes, Engineering a
Practical Retrieve Superstep, Connecting to Other Data Sources,

12
III AssessSuperstep: AssessSuperstep,Errors,AnalysisofData, Practical
Actions, Engineering a Practical Assess Superstep, 12

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 Apply quantitative modeling and data analysis techniques to the
solution of real world business problems, communicate findings, and
effectively present results using data visualization techniques.
 Recognize and analyze ethical issues in businessrelated to intellectual
property, data security, integrity, and privacy. Course Outcome IV Process Superstep : Data Vault, Time -Person -Object -Location -Event
Data Vault, Data Science Process, Data Science,
Transform Superstep : Transform Superstep, Building a Data
Warehouse, Transforming with Data Science, Hypothesis Testing,
Overfitting and Underfitting, Precision -Recall, Cross -Validation Test.
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V Transform Superstep: Univariate Analysis, Bivariate Analysis,
Multivariate Analysis, Linear Regression, Logistic Regression,
Clustering Techniques, ANOVA, Principal Component Analysis
(PCA),DecisionTrees,SupportVectorMachines,Networks,Clusters, and
Grids, Data Mining, Pattern Recognition, Machine Learning, Bagging
Data,Random Forests, Computer Vision (CV) , Natural Language
Processing (NLP), Neural Networks, TensorFlow.
Organize and Report Supersteps : Organize Superstep, Report
Superstep, Graphics, Pictures, Showing the Difference


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Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. PracticalData Science AndreasFrançois
Vermeulen APress 2018
2. PrinciplesofData Science Sinan Ozdemir PACKT 2016
3. DataSciencefrom Scratch Joel Grus O’Reilly 2015
4. DataSciencefromScratch first
Principle in python Joel Grus Shroff
Publishers 2017
5. ExperimentalDesignin
Datasciencewith Least
Resources NCDas Shroff
Publishers 2018


M.Sc(Information Technology) Semester – I
CourseName: DataScience Practical CourseCode: PSIT1P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

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 Applyethicalpracticesineverydaybusinessactivitiesandmakewell -
reasoned ethical business and data management decisions.
 Demonstrateknowledgeofstatisticaldataanalysistechniquesutilized in
business decision making.
 Apply principlesofDataSciencetotheanalysisofbusiness problems.
 Usedataminingsoftwaretosolvereal -world problems.
 Employcuttingedgetoolsand technologiestoanalyzeBig Data.
 Applyalgorithmstobuildmachine intelligence.
 Demonstrateuseofteamwork,leadershipskills,decisionmak ingand
organization theory.

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TolearnhowtouseCloudServices. To
implement Virtualization.
To implement Task Scheduling algorithms.
ApplyMap -Reduceconcepttoapplications.
To build Private Cloud.
Broadly educatetoknowtheimpactofengineeringonlegaland
societal issues involved. Objectives M.Sc(Information Technology) Semester – I
CourseName:Cloud Computing CourseCode: PSIT103
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Cloud Computing: Introduction, Historical
developments,BuildingCloudComputingEnvironments, Principlesof
ParallelandDistributed Computing: ErasofComputing,Parallelv/s
distributed computing, Elements of Parallel Computing, Elements of
distributed computing, Technologies for distributed computing.
Virtualization: Introduction, Characteristics of virtualized
environments, Taxonomy of v irtualization techniques, Virtualization
and cloud computing, Pros and cons of virtualization, Technology
examples.LogicalNetworkPerimeter,VirtualServer,Cloud Storage
Device,Cloudusagemonitor,Resourcereplication, Ready -made
environment.



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II Cloud ComputingArchitecture: Introduction,Fundamentalconcepts
andmodels,Rolesandboundaries,CloudCharacteristics,Cloud Delivery
models, Cloud Deployment models, Economics of the cloud,
Openchallenges. FundamentalCloudSecurity: Basics,Threat
agents,Cloudsecuritythrea ts,additionalconsiderations. Industrial
Platforms and New Developments: Amazon Web Services,Google
App Engine, Microsoft Azure.

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III Specialized Cloud Mechanisms: Automated Scaling listener, Load
Balancer, SLA monitor, Pay -per-use monitor, Audit monitor, fail over
system, Hypervisor, Resource Centre, Multidevice broker, State
Management Database. Cloud Management Mechanisms: Remote
administration system, Resource Ma nagement System, SLA
Management System, Billing Management System, Cloud Security
Mechanisms: Encryption, Hashing, Digital Signature, PublicKey
Infrastructure(PKI),IdentityandAccessManagement(IAM), Single

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Sign-On(SSO),Cloud -Based SecurityGroups,HardenedVirtual Server
Images
IV Fundamental Cloud Architectures: Workload Distribution
Architecture, Resource Pooling Architecture, Dynamic Scalability
Architecture, Elastic Resource Capacity Architecture, Service Load
Balancing Architecture, Cloud Bursting Architecture, Elastic Disk
ProvisioningArchitecture,RedundantStorageArchitecture. Advanced
Cloud Architectures: Hypervisor Clustering Architecture, Load
Balanced Virtual Server Instances Architecture, Non -Disruptive
Service Relo cation Architecture, Zero Downtime Architecture, Cloud
Balancing Architecture, Resource Reservation Architecture, Dynamic
Failure DetectionandRecoveryArchitecture,Bare -Metal Provisioning
Architecture,RapidProvisioningArchitecture,StorageWorkload
Management Architecture



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V CloudDelivery ModelConsiderations: CloudDeliveryModels:The
CloudProviderPerspective,CloudDeliveryModels:TheCloud Consumer
Perspective, Cost Metrics and Pricing Models : Business Cost
Metrics, Cloud Usage Cost Metrics, Cost Management
Considerations, Service Quality Metrics and SLAs: Service Quality
Metrics, SLA Guidelines

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Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Mastering
Cloud ComputingFoundatio
nsand Applications
Programming RajkumarBuyya,
Christian
Vecchiola, S.
Thamarai Selvi Elsevier - 2013
2. Cloud Computing
Concepts,Technology& Arc
hitecture ThomasErl,
Zaigham
Mahmood,
andRicardo
Puttini Prentice
Hall - 2013
3. Distributed and Cloud
Computing, From Parallel
ProcessingtotheInternetof
Things Kai Hwang, Jack
Dongarra,Geoffrey
Fox MK
Publishers -- 2012

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 AnalyzetheCloudcomputingsetupwithitsvulnerabilitiesand
applications using different architectures.
 Designdifferentworkflowsaccordingtorequirementsandapply
map reduce programming model.
 ApplyanddesignsuitableVirtualizationconcept,CloudResource
Management and design scheduling algorithms.
 Createcombinatorialauct ionsforcloudresourcesanddesign
scheduling algorithms for computing clouds
 AssesscloudStoragesystemsandCloudsecurity,therisks
involved, its impact and develop cloud application
 Broadly educate to know the impact of engineering on legal and
societalissuesinv olvedinaddressingthesecurityissues ofcloud
computing. Course Outcome M.Sc(Information Technology) Semester – I
CourseName:CloudComputing Practical CourseCode: PSIT1P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

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problem Allthesetechniqueswillbemoreeffectivetosolvethe efficiently • • Softcomputingconceptslikefuzzylogic,neuralnetworksandgenetic
algorithm, where Artificial Intelligence is mother branch of all. Objectives
BasicconceptsofArtificialIntelligence.Knowledgeof Algorithms Pre requisites M.Sc(Information Technology) Semester – I
CourseName:SoftComputing Techniques CourseCode: PSIT104
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction of soft computing, soft computing vs. hard computing,
various types of soft computing techniques, Fuzzy Computing, Neural
Computing, Genetic Algorithms, Associative Memory, Adaptive
Resonance Theory, Classification, Clustering, Bayesian Networks,
Probabilistic reasoning, applications of soft computing.

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II Artificial Neural Network: Fundamental concept, Evolution of Neural
Networks, Basic Models, McCulloh -Pitts Neuron, Linear Separability,
Hebb Network.
Supervised Learning Network: Perceptron Networks, Adaptive Linear
Neuron,MultipleAdaptiveLinearNeurons,BackpropagationNetwork,
Radial BasisFunction,TimeDelayNetwork,FunctionalLinkNetworks,
Tree Neural Network.
Associative Memory Networks: Training algorithm for pattern
Association, Autoassociative memory network, hetroassociative
memory network, bi -directional associative memory, Hopfield
networks, iterative autoassociative memory networks, temporal
associative memory networks.



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III UnSupervised Learning Networks: Fixed weight competitive nets,
Kohonen self -organizing feature maps, learning vectors quantization,
counter propogation networks, adaptive resonance theory networks.
Special Networks: Simulated annealing, Boltzman machine, Gaussian
Mach ine,CauchyMachine,Probabilisticneuralnet,cascadecorrelation
network, cognition network, neo -cognition network, cellular neural
network, optical neural network
ThirdGenerationNeural Networks:
SpikingNeuralnetworks,convolutionalneuralnetworks,deeplearning
neural networks, extreme learning machine model.



12

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IV IntroductiontoFuzzyLogic,ClassicalSetsandFuzzysets: Classical
sets, Fuzzy sets.
ClassicalRelationsandFuzzy Relations:
CartesianProductofrelation,classicalrelation,fuzzyrelations,
tolerance and equivalence relations, non -iterative fuzzy sets.
Membership Function: features of the membership functions,
fuzzification, methods of membership value assignments.
Defuzzification:Lambda -cutsforfuzzysets,Lambda -cutsforfuzzy
relations, Defuzzification methods.
FuzzyArithmeticandFuzzymeasures:fuzzyarithmetic,fuzzy
measures, measures of fuzziness, fuzzy integrals.



12
V FuzzyRulebaseandApproximate reasoning:
Fuzzy proportion, formation of rules, decomposition of rules,
aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems,
Fuzzy logic control systems, control system design, architecture and
operation of FLC system, FLC system models and applicat ions of FLC
System.
Genetic Algorithm: Biological Background, Traditional optimization
and search techniques, genetic algorithm and search space, genetic
algorithmvs.traditionalalgorithms,basicterminologies,simplegenetic
algorithm, general genetic algorith m, operators in genetic algorithm,
stopping condition for genetic algorithm flow, constraints in genetic
algorithm, problem solving using genetic algorithm, the schema
theorem,classificationofgeneticalgorithm,Hollandclassifiersystems,
genetic programming, advantages and limitations and applications of
genetic algorithm.
Differential Evolution Algorithm, Hybrid soft computing techniques –
neuro –fuzzyhybrid,geneticneuro -hybridsystems,genetic fuzzy
hybrid and fuzzy genetichybrid systems.





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Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. ArtificialIntelligenceand Soft
Computing Anandita Das
Battacharya SPD 3rd 2018
2. PrinciplesofSoft computing S.N.SivanandamS
.N.Deepa Wiley 3rd 2019
3. Neuro -Fuzzy and Soft
Computing J.S.R.Jang,
C.T.Sunand
E.Mizutani Prentice
Hall of
India 2004
4. NeuralNetworks,Fuzzy
Logic and Genetic
Algorithms:Synthesis& Appli
cations S.Rajasekaran,
G.A.
Vijayalakshami Prentice
Hall of
India 2004
5. Fuzzy Logic with
EngineeringApplications Timothy J.Ross McGraw -
Hill 1997

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• Identifyanddescribesoftcomputingtechniquesandtheirrolesin
building intelligent machines
• Recognizethefeasibilityofapplyingasoftcomputingmethodology for
a particular problem
• Applyfuzzylogicandreasoningtohandleuncertaintyandsolve
engineering problems
• Applygeneticalgorithmstocombinatorialoptimization problems
• Applyneuralnetworksforclassificationandregression problems
• Effectivelyuseexistingsoftwaretoolstosolverealproblemsusing a
soft computing approach
• Evaluateandcomparesolutionsby varioussoftcomputing
approaches for a given problem. Course Outcome 6. GeneticAlgorithms: Search,
OptimizationandMachine
Learning Davis
E.Goldberg Addison
Wesley 1989
7. IntroductiontoAIand
Expert System Dan W.
Patterson Prentice
Hallof
India 2009

M.Sc(Information Technology) Semester – I
CourseName:SoftComputingTechniques Practical CourseCode: PSIT1P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus, coveringentire syllabus

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SEMESTER II

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 Toprovideanoverviewofanexcitinggrowingfieldofbigdata analytics.
 Tointroducethetoolsrequiredtomanageandanalyzebigdatalike
Hadoop, NoSql MapReduce.
 Toteachthefundamentaltechniquesandprinciplesinachievingbigdata
analytics with scalability and streaming capability.
 Toenablestudentstohaveskillsthatwillhelpthemtosolvecomplexreal - world
problems in for decision support. Objectives M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics CourseCode: PSIT201
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Big Data, Characteristics of Data, and Big Data
Evolution of Big Data, Definition of Big Data, Challenges with big
data, Why Big data? Data Warehouse environment, Traditional
Business Intelligence versus Big Data. State of Practice in Analy tics,
Key roles for New Big Data Ecosystems, Examples of big Data
Analytics.
BigDataAnalytics,Introductiontobigdataanalytics,Classificationof
Analytics, Challenges of Big Data, Importance of Big Data, Big Data
Technologies, Data Science, Responsibilities, Soft state eventual
consistency. Data Analytics Life Cycle


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II Analytical Theory and Methods: Clustering and Associated
Algorithms, Association Rules, Apriori Algorithm, Candidate Rules,
ApplicationsofAssociationRules,Validationand Testing,
Diagnostics,Regression,LinearRegression,LogisticRegression,
Additional Regression Models.
12
III Analytical Theory and Methods: Classification, Decision Trees, Naïve
Bayes, Diagnostics of Classifiers, Additional Classification Methods,
TimeSeries Analysis,BoxJenkinsmethodology,ARIMAModel,
Additionalmethods.TextAnalysis,Steps,TextAnalysisExample,
Collecting Raw Text, Representing Text, Term Frequency -Inverse
DocumentFrequency(TFIDF),CategorizingDocumentsbyTopics,
Determining Sentiments

12
IV Data Product, Building Data Products at Scale with Hadoop, Data
Science Pipeline and Hadoop Ecosystem, Operating System for Big
Data, Concepts, Hadoop Architecture, Working with Distributed file
system,WorkingwithDistributedComputation,Frameworkfor Python
andHadoopStreaming,HadoopStreaming,MapReducewith Python,
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applications etc. Understand the key issues in big data management and its
associated applications in intelligent business and scientific
computing.
Acquire fundamental enabling techniques and scalable
algorithms like Hadoop, Map Reduce and NO SQL in bi g data
analytics.
Interpret business models and scientific computing paradigms,
and apply software tools for big data analytics.
Achieve adequate perspectives of big data analytics in various
applicationslikerecommendersystems,social media •


•


•

• Course Outcome Advanced MapReduce. In -Memory Computing with Spark, Spark
Basics,InteractiveSparkwithPySpark,WritingSparkApplications,
V Distributed Analysis and Patterns, Computing with Keys, Design
Patterns, Last -Mile Analytics, Data Mining and Warehousing,
StructuredDataQuerieswithHive,HBase,DataIngestion, Importing
RelationaldatawithSqoop,Injestingstreamdatawithflume.Analytics
with higher level APIs, Pig, Spark’s higher level APIs.
12
,
Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Big Data and Analytics Subhashini
Chellap panSee
maAcharya Wiley First
2. DataAnalyticswith Hadoop
AnIntroductionforData
Scientists Benjamin
Bengfortand
Jenny Kim O’Reilly 2016
3. Big Data and Hadoop V.KJain Khanna
Publishing First 2018

M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics Practical CourseCode: PSIT2P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

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 Tounderstandthestate -of-the-artinnetworkprotocols,architecturesand
applications.
 Analyzeexistingnetworkprotocolsand networks.
 Developnewprotocolsin networking
 Tounderstandhownetworkingresearchis done
 ToinvestigatenovelideasintheareaofNetworkingvia term-longresearch
projects. Objectives
Fundamentalsof Networking Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Modern Networking CourseCode: PSIT202
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Modern Networking
ElementsofModern Networking
The Networking Ecosystem ,Example Network Architectures,Global
Network Architecture,A Typical Network Hierarchy Ethernet
Applications of Ethernet Standards Ethernet Data Rates Wi -Fi
ApplicationsofWi -Fi,StandardsWi -FiDataRates4G/5GCellularFirst
Generation Second Generation, Third Generation Fourth Generation
Fifth Generation, Cloud Computin g Cloud Computing Concepts The
Benefits of Cloud Computing Cloud Networking Cloud Storage,
Internet of Things Things on the Internet of Things, Evolution Layers
of the Internet of Things, Network Convergence Unified
Communications, Requirements and Technol ogy Types of Network
andInternetTraffic,ElasticTraffic,InelasticTraffic,Real -TimeTraffic
Characteristics Demand: Big Data, Cloud Computing, and Mobile
TrafficBig Data Cloud Computing,,Mobile Traffic, Requirements:
QoS and QoE,,Quality of Service,Quality of Experience, Routing
Characteristics, Packet Forwarding, Congestion Control ,Effects of
Congestion,CongestionControlTechniques,SDNandNFV Software -
DefinedNetworking,NetworkFunctionsVirtualizationModern
Networking Elements







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II Software -Defined Networks
SDN: Background and Motivation, Evolving Network Requirements
Demand Is Increasing,Supply Is IncreasingTraffic Patterns Are More
ComplexTraditionalNetworkArchitecturesareInadequate,The SDN
ApproachRequirementsSDNArchitectureCharacteristicsof Softwar e-

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Defined Networking, SDN - and NFV -Related Standards Standards -
Developing Organizations Industry Consortia Open Development
Initiatives, SDN Data Plane and OpenFlow SDN Data Plane, Data
Plane Functions Data Plane Protocols OpenFlow Logical Network
DeviceFlowTableStructureFlowTablePipeline,TheUseofMultiple
Tables Group Table OpenFlow Protocol, SDN Control Plane
SDNControlPlaneArchitectureControlPlaneFunctions,Southbound
Interface Northbound InterfaceRouting, ITU -T Model,
OpenDaylight OpenDaylightArchitectureOpenDaylightHelium,RESTR
ESTConstraintsExampleRESTAPI,CooperationandCoordination
Among Controllers, Centralized Versus DistributedControllers, High -
AvailabilityClustersFederatedSDNNetworks,BorderGateway
ProtocolRoutingandQoSBetweenD omains,UsingBGPforQoS
ManagementIETFSDNiOpenDaylightSNDi SDNApplication Plane
SDNApplicationPlaneArchitectureNorthboundInterfaceNetwork
ServicesAbstractionLayerNetworkApplications,UserInterface,
NetworkServicesAbstractionLayerAbstractionsinSDN,Frenetic Tra ffic
Engineering PolicyCop Measurement and Monitoring Security
OpenDaylight DDoS Application Data Center Networking, Big Data
overSDNCloudNetworkingoverSDNMobilityandWireless
Information -Centric Networking CCNx, Use of an Abstraction Layer
III Virtualization, Network Functions Virtualization: Concepts and
Architecture, Background and Motivation for NFV, Virtual Machines
The Virtual Machine Monitor, Architectural Approaches Container
Virtualization, NFV Concepts Simple Example of the Use of NFV,
NFV Principles High -Level NFV Framework, NFV Benefits and
RequirementsNFVBenefits,NFVRequirements, NFV Reference
Architecture NFV Management and Orchestration, Reference Points
Implementation, NFV Functionality, NFV
Infrastructure,ContainerInterface,Depl oyment of NFVI
Containers,Logical Structure of NFVI Domains,ComputeDomain,
Hypervisor Domain,Infrastructure Network
Domain, Virtualized Network Functions, VNF
Interfaces,VNFC to VNFC Communication,VNF Scaling, NFV
Management and Orchestration, Virtualized Infrastructure
Manager,Virtual Network Function Manager,NFV Orchestrator,
Repositories, Element Management, OSS/BSS, NFV Use Cases
Architectural Use Cases, Service -Oriented Use Cases, SDN and NFV
Network Virtualization, Virtual LANs ,The Use of Virtual
LANs,Defining VLANs, Communicating VLAN Membership,IEEE
802.1Q VLAN Standard, Nested VLANs, OpenFlow VLAN Support,
VirtualPrivateNetworks, IPsec VPNs,MPLS VPNs, Network
Virtualization, Simplified Example, Network Virtualization
Arch itecture, Benefits of Network Virtualization, OpenDaylight’s
Virtual Tenant Network, Software -Defined Infrastructure,Software -
DefinedStorage,SDI Architecture









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Page 24

IV DefiningandSupportingUserNeeds,QualityofService,Background,
QoSArchitectural Framework,DataPlane,Control Plane,Management
Plane, Integrated Services Architecture, ISA Approach
ISA Components, ISA Services, Queuing Discipline, Differentiated
Services, Services, DiffServ Field, DiffServ Configuration and
Operation,Per -HopBehavior,DefaultForwardingPHB,ServiceLevel
Agreements, IP Performance Metrics, OpenFlow QoS Support, Queue
Structures, Meters, QoE: User Quality of Experience, Why
QoE?,Online Video Content Delivery, Service Failures Due to
InadequateQoEConsiderationsQoE -RelatedStandardizationProjects,
Definition of Quality of Experience, Definition of Quality, Definition
of Experience Quality Formation Process, Definition of Quality of
Experience, QoE Strategies in Practice, The QoE/QoS Layered Model
Summarizing and Merg ing the ,QoE/QoS Layers, Factors Influencing
QoE, Measurements of QoE, Subjective Assessment, Objective
Assessment, End -User Device Analytics, Summarizing the QoE
Measurement Methods, Applications of QoE Network Design
Implications of QoS and QoE Classific ation of QoE/ QoS Mapping
Models, Black -Box Media -Based QoS/QoE Mapping Models, Glass -
BoxParameter -BasedQoS/QoEMappingModels,Gray -BoxQoS/QoE
Mapping Models, Tips for QoS/QoE Mapping Model Selection,IP -
Oriented Parameter -Based QoS/QoE Mapping Models,Netwo rk Layer
QoE/QoS Mapping Models for Video Services, Application Layer
QoE/QoS Mapping Models for Video Services Actionable QoE over
IP-Based Networks, The System -Oriented Actionable QoE Solution,
The Service -Oriented Actionable QoE Solution, QoE Versus QoS
Service Monitoring, QoS Monitoring Solutions, QoE Monitoring
Solutions,QoE -BasedNetworkandServiceManagement,QoE -Based
Management of VoIP Calls, QoE -Based Host -Centric Vertical
Handover, QoE -Based Network -Centric Vertical Handover












12
V Modern Network Architecture: Clouds and Fog, Cloud Computing,
Basic Concepts, Cloud Services, Software as a Service, Platform as a
Service,InfrastructureasaService,OtherCloudServices,XaaS,Cloud
Deployment Models, Public Cloud Private Cloud Community Cloud,
Hybri d Cloud, Cloud Architecture, NIST Cloud Computing Reference
Architecture,ITU -T Cloud Computing Reference Architecture, SDN and
NFV, Service Provider Perspective Private Cloud Perspective, ITU -T
Cloud Computing Functional Reference Architecture, The Interne t of
Things: Components The IoT Era Begins, The Scope of the Internet of
Things Components of IoT -Enabled Things, Sensors, Actuators,
Microcontrollers, Transceivers, RFID, The Internet of Things:
Architecture and Implementation, IoT Architecture,ITU -T IoT
Reference Model, IoT World Forum Reference Model, IoT
Implementation, IoTivity, Cisco IoT System, ioBridge, Security
Security Requirements, SDN Security Threats to SDN, Software -
Defined Security, NFV Security, Attack Surfaces, ETSI Security
Perspective,Se curityTechniques,CloudSecurity,SecurityIssues and
Concerns,CloudSecurityRisksandCountermeasures,Data Protection






12

Page 25

 Demonstratein -depthknowledge intheareaofComputer Networking.
 To demonstratescholarship of knowledge through performing in agroup
to identify, formulate and solve a problem related to ComputerNetworks
 Prepare a technical document for the identified Networking System
Conducting experiments to analyze the identified research work in
building Computer Networks Course Outcome intheCloud,Cloud SecurityasaService,AddressingCloudComputer
SecurityConcerns,IoTSecurity,ThePatchingVulnerability, IoT
SecurityandPrivacyRequirementsDefinedbyITU -TAnIoT Security
Framework, Conclusion

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Foundationsof Modern
Networking:SDN,NFV,
QoE, IoT, and Cloud William
Stallings Addison -
Wesley
Professional October
2015
2. SDNandNFVSimplified A
Visual Guide to
Understanding Software
Defined Networks and
NetworkFunction
Virtualization Jim Doherty Pearson
Education,
Inc
3. Network Functions
Virtualization(NFV)
withaTouchof SDN Rajendra
Chayapathi
SyedFarrukh
Hassan Addison -
Wesley
4. CCIEandCCDEEvolving
Technologies Study
Guide Braddgeworth,
Jason Gooley,
RamiroGarza
Rios Pearson
Education,
Inc 2019

M.Sc(Information Technology) Semester – II
CourseName:ModernNetworking Practical CourseCode: PSIT2P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 26

Gainathoroughunderstandingofthephilosophyandarchitectureof Web
applications using ASP.NET Core MVC;
Gainapracticalunderstandingof.NET Core;
AcquireaworkingknowledgeofWebapplicationdevelopmentusing
ASP.NET Core MVC 6 and Visual Studio
PersistdatawithXMLSerializationandADO.NETwithSQLServer Create
HTTP services using ASP.NET Core Web API;
DeployASP.NETCoreMVCapplicationstotheWindows Azure
cloud. Objectives M.Sc(Information Technology) Semester – I
CourseName:Microservice Architecture CourseCode: PSIT203
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Microservices: Understanding Microservices, Adopting
Microservices, The Microservices Way. Microservices Value
Proposition: DerivingBusinessValue,definingaGoal -
Oriented,LayeredApproach,Applyingthe Goal -
Oriented,LayeredApproach. Designing Microservice Systems:
The Systems Approach to Microservices, A
Microservices Design Process, Establishing a Foundation: Goals
and Principles, Platforms, Culture.

12
II Service Design: Microservice Boundaries, API design for
Microservices, Data and Microservices, Distributed Transactions and
Sagas, Asynchronous Message -Passing and Microservices, dealing
with Dependencies, System Design and Operations: Independent
Deployability, More Serv ers, Docker and Microservices, Role of
ServiceDiscovery,NeedforanAPIGateway,MonitoringandAlerting.
AdoptingMicroservicesinPractice: SolutionArchitecture Guidance,
OrganizationalGuidance,CultureGuidance,ToolsandProcess
Guidance, Services Guidance.


12
III Building Microservices with ASP.NET Core: Introduction,
Installing .NET Core, Building a Console App, Building ASP.NET
Core App. Delivering Continuously: Introduction to Docker,
Continuous integration with Wercker, Continuous Integration with
Circle CI, De ploying to Dicker Hub. Building Microservice with
ASP.NETCore: Microservice,TeamService,APIFirstDevelopment,
Test First Controller, Creating a CI pipeline, Integration Testing,
RunningtheteamserviceDockerImage. Backing Services:


12

Page 27

MicroservicesEcosystems,BuildingthelocationService,Enhancing
Team Service.
IV Creating Data Service: Choosing a Data Store , Building a Postgres
Repository, Databases are Backing Services, Integration Testing Real
Repositories, Exercise the Data Service. Event Sourcing and CQRS:
Event Sourcing, CQRS pattern, Event Sourcing and CQRS, Running
the samples. Building an ASP.NET Core Web Application:
ASP.NET Core Basics, Building Cloud -Native Web Applications.
ServiceDiscovery: CloudNativeFactors,NetflixEureka, Discovering
and Advertising ASP.NET Core Services. DNS and Platform Supported
Discovery.


12
V Configuring Microservice Ecosystems: Using Environment
VariableswithDocker,UsingSpringCloudConfigServer,Configuring
Microservices with etcd, Securing Applications and Microservices:
Security in the Cloud, Securing ASP.NET Core Web Apps, Securing
ASP.NET Core Microservices. Building Real -Time Apps and
Services: Real-Time Applications Defined, Websockets in the Cloud,
Using a Cloud Messaging Prov ider, Building the Proximity Monitor.
Putting It All Together: Identifying and Fixing Anti -Patterns,
Continuing the Debate over Composite Microservices, The Future.


12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Microservice Architecture:
Aligning Principles,
Practices, and Culture Irakli
Nadareishvili,
Ronnie Mitra,
MattMcLarty,
and Mike
Amundsen O’Reilly First 2016
2. BuildingMicroserviceswith
ASP.NET Core Kevin Hoffman O’Reilly First 2017
3. Building Microservices:
DesigningFine -Grained
Systems SamNewman O’Reilly First
4. Production -ready
Microservices SusanJ. Fowler O’Reilly 2016

Page 28

DevelopwebapplicationsusingModelView Control.
CreateMVCModelsandwritecodethatimplementsbusinesslogic
within Model methods, properties, and events.
CreateViewsinanMVCapplicationthatdisplay andeditdataand interact
with Models and Controllers.
Boostyourhireabilitythroughinnovativeandindependent learning.
Gaining a thorough understanding of the philosophy and
architecture of .NET Core
Understanding packages, metapackages and frameworks
Acquirin gaworkingknowledgeofthe.NETprogrammingmodel
Implementing multi -threading effectively in .NET applications Course Outcome M.Sc (Information Technology) Semester – II
CourseName:MicroservicesArchitecture Practical CourseCode: PSIT2P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 29

 Reviewthefundamentalconceptsofadigitalimageprocessing
system.
 Analyzeimagesinthefrequencydomainusingvarious transforms.
 Evaluatethetechniques forimageenhancementandimage restoration.
 Categorizevariouscompression techniques.
 InterpretImagecompression standards.
 Interpretimagesegmentationandrepresentation techniques. Objectives M.Sc(Information Technology) Semester – II
CourseName: Image Processing CourseCode: PSIT204
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40



Unit Details Lectures
I Introduction: DigitalImageProcessing, OriginsofDigitalImageProcessing,
Applications and Examples of Digital Image Processing, Fundamental Steps
in Digital Image Processing, Components of an Image Processing System,
DigitalImageFundamentals: ElementsofVisualPerception,Lightandthe
Electromagneti c Spectrum, Image Sensing andAcquisition, Image Sampling
and Quantization, Basic Relationships Between Pixels, Basic Mathematical
Tools Used in Digital Image Processing, Intensity Transformations and
Spatial Filtering: Basics, Basic Intensity Transformation Functions, Basic
Intensity Transformation Functions, Histogram Processing, Fundamentals of
Spatial Filtering, Smoothing (Lowpass) Spatial Filters, Sharpening
(Highpass)SpatialFilters,Highpass,Bandreject,andBandpassFilters from
LowpassFilters, CombiningSpatialEnhancementMethods,UsingFuzzy
Techniques for Intensity Transformations and Spatial Filtering




12
II Filtering in the Frequency Domain: Background, Preliminary Concepts,
Sampling and the Fourier Transform of Sampled Functions, The Discrete
Fourier Transform of One Variable, Extensions to Functions of Two
Variables, Properties of the 2 -D DFT and IDFT, Basics of Filtering in the
Frequency Do main, Image Smoothing Using Lowpass Frequency Domain
Filters, Image Sharpening Using Highpass Filters, Selective Filtering, Fast
Fourier Transform
Image Restoration and Reconstruction: A Model of the Image
Degradation/Restoration Process, Noise Models, Restoration in the Presence
of Noise Only -----Spatial Filtering, Periodic Noise Reduction Using
Frequency Domain Filtering, Linear, Position -Invariant Degradations,
EstimatingtheDegradationFunction,InverseFiltering,Minimum Mean
SquareError(Wiener)Filtering, ConstrainedLeastSquaresFiltering,
Geometric Mean Filter, Image Reconstruction from Projections




12
III WaveletandOtherImageTransforms: Preliminaries,Matrix -based
Transforms,Correlation,BasisFunctionsintheTime -FrequencyPlane, Basis 12

Page 30

Images, Fourier -Related Transforms, Walsh -Hadamard Transforms, Slant
Transform, Haar Transform, Wavelet Transforms
Color Image Processing: Color Fundamentals, Color Models, Pseudocolor
Image Processing, Full -Color Image Processing, Color Transformations,
ColorImageSmoothingandSharpening,UsingColorinImageSegmentation,
Noise in Color Images, Color Image Compression.
ImageCompressionandWatermarking: Fundamentals,HuffmanCoding,
GolombCoding,ArithmeticCoding,LZWCoding,Run -length Coding,
Symbol -basedCoding,8 Bit-planeCoding,BlockTransformCoding,
Predictive Coding, Wavelet Coding, Digital Image Watermarking,
IV Morphological Image Processing: Preliminaries, Erosion and Dilation,
Opening and Closing, The Hit -or-Miss Transform, Morphological
Algorithms, Morphol ogical Reconstruction¸ Morphological Operations on
Binary Images, Grayscale Morphology
ImageSegmentationI:EdgeDetection,Thresholding,andRegion Detection:
Fundamentals, Thresholding, Segmentation by Region Growing
andbyRegionSplittingandMerging,Region SegmentationUsingClustering and
Superpixels, Region Segmentation Using Graph Cuts, Segmentation Using
Morphological Watersheds, Use of Motion in Segmentation


12
V Image Segmentation II: Active Contours: Snakes and Level Sets:
Background, Image Segmentation Using Snakes, Segmentation Using Level
Sets.
Feature Extraction: Background, Boundary Preprocessing, Boundary
FeatureDescriptors,RegionFeatureDescriptors,PrincipalComponents as
FeatureDescriptors,Whole -ImageFeatures,Scale -InvariantFeature
Transform (SIFT)

12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. DigitalImage Processing Gonzalezand
Woods Pearson/Prentice
Hall Fourth 2018
2. FundamentalsofDigital
Image Processing AK.Jain PHI
3. TheImage Processing
Handbook J.C.Russ CRC Fifth 2010

M.Sc(Information Technology) Semester – II
CourseName:ImageProcessing Practical CourseCode: PSIT2P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 31

 Understandtherelevantaspectsofdigitalimagerepresentationand
their practical implications.
 Havetheabilitytodesignpointwiseintensitytransformations tomeet
stated specifications.
 Understand2 -Dconvolution,the2 -DDFT,andhavetheabitiltyto
design systems using these concepts.
 Haveacommandofbasicimagerestoration techniques.
 Understandtheroleofalternativecolorspaces,andthedesign
requirements leading to choices of color space.
 Appreciatetheutilityofwaveletdecompositionsandtheirroleinimage
processing systems.
 Haveanunderstandingoftheunderlyingmechanismsofimage
compression, and the ability to design systems usingstandard
algorithms to meet design specifications. Course Outcome

Page 32

Evaluation Scheme
InternalEvaluation(40 Marks)
Theinternalassessmentmarksshallbeawardedas follows:
1. 30marks(Anyoneofthe following):
a. WrittenTest or
b. SWAYAM(AdvancedCourse) ofminimum20hoursandcertificationexam
completed or
c. NPTEL(AdvancedCourse)ofminimum20hoursandcertificationexam
completed or
d. ValidInternationalCertifications(Prometric,Pearson,Certiport,Coursera,
Udemy and the like)
e. Onecertificationmarksshallbeawardedonecourse only.Forfourcourses, the
students will have to complete four certifications.
2. 10 marks
Themarksgivenoutof40forpublishingtheresearchpapershouldbedividedinto four
course and should awarded out of 10 in each of the four course.

i. SuggestedformatofQuestionpaper of30marksforthewritten test.
Q1. Attempt anytwo ofthe following: 16
a.
b.
c.
d.

Q2. Attempt anytwo ofthe following: 14
a.
b.
c.
d.

ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on
publishing of research paper in UGC approved Journal with plagiarism less than
10%.Themarkscanbeawardedaspertheimpactfactorofthejournal,qualityof the
paper, importance of the contents published, social value.

Page 33

32 ExternalExamination:(60
marks)


Allquestionsare compulsory
Q1 (BasedonUnit1)Attempt anytwo ofthe following: 12
a.
b.
c.
d.

Q2 (BasedonUnit2)Attempt anytwo ofthe following: 12
Q3 (BasedonUnit3)Attempt anytwo ofthe following: 12
Q4 (BasedonUnit4)Attempt anytwo ofthe following: 12
Q5 (Basedon Unit5)Attempt anytwo ofthe following: 12
PracticalEvaluation(50 marks)
ACertifiedcopyjournalisessentialtoappearforthepractical examination.

1. PracticalQuestion 1 20
2. PracticalQuestion 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5

Page 34

33

Artificial Intelligence - Major

Semester III Compulsory Course
Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT301 Technical Writing and
Entrepreneurship
Development 60 4 PSIT3P1 60 2 6

MSc IT with specialization in Artificial Intelligence [MSc IT(AI)]
Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT302a Applied Artificial
Intelligence 60 4 PSIT3P2a 60 2 6
PSIT303a Machine Learning 60 4 PSIT3P3b 60 2 6
PSIT304a Robotic Process
Automation 60 4 PSIT3P3c 60 2 6

Semester IV Compuslory Course
Paper code Paper Lectures Credit Practical Hrs Credit Total Credit
Nomenclature Paper
PSIT401 Blockchain 60 4 PSIT4P1 60 2 6

Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT402a Natural Language
Processing 60 4 PSIT4P2a 60 2 6
PSIT403a Deep Learning 60 4 PSIT4P3a 60 2 6
PSIT404a Human Computer
Interaction 60 4 PSIT4P4a 60 2 6

PSIT4P4 - Project Implementation and Viva

Page 35

34



















SEMESTER III

Page 36

35


PSIT301: Technical Writing and
Entrepreneurship Development

M. Sc (Information Technology) Semester – III
Course Name: Technical Writing and Entrepreneurship
Development Course Code: PSIT301
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 This course aims to provide conceptual understanding of developing strong foundation in general writing,
including research proposal and reports.
 It covers the technological developing skills for writing Article, Blog, E -Book, Commercial web Page
design, Business Listing Press Releas e, E-Listing and Product Description.
 This course aims to provide conceptual understanding of innovation and entrepreneurship development.

Unit Details Lectures Outcome
I Introduction to Technical Communication:
What Is Technical Communication? The Challenges of
Producing Technical Communication, Characteristics of a
Technical Document , Measures of Excellence in Technical
Documents, Skills and Qualities Shared by Successful
Workplace Communicators, How Communication Skills and
Qualities Affect Your C areer? Understanding Ethical and
Legal Considerations: A Brief Introduction to Ethics, Your
Ethical Obligations, Your Legal Obligations, The Role of
Corporate Culture in Ethical and Legal
Conduct,Understanding Ethical and Legal Issues Related to
Social Medi a, Communicating Ethically Across Cultures,
Principles for Ethical Communication Writing Technical
Documents:
Planning, Drafting, Revising, Editing, Proofreading Writing
Collaboratively: Advantages and Disadvantages of
Collaboration, Managing Projects, Conducting Meetings,
Using Social Media and Other Electronic Tools in
Collaboration, Importance of Word Press Website, Gender and
Collaboration, Culture and Collaboration . 12 CO1
II Introdu ction to Content Writing: Types of Content (Article,
Blog, E -Books, Press Release, Newsletters Etc), Exploring
Content Publication Channels. Distribution of your content
across various channels. Blog Creation: Understand the
psychology behind your web traffic , Creating killing landing
pages which attract users, Using Landing Page Creators,
Setting up Accelerated Mobile Pages, Identifying UI UX
Experience of your website or blog. Organizing Your 12 CO2

Page 37

36 Information: Understanding Three Principles for
Organizing Technical Information, Understanding
Conventional Organizational Patterns, Emphasizing
Important Information: Writing Clear, Informative Titles,
Writing Clear, Informative Headings, Writing Clear
Informative Lists, Writing Clear Informative Paragraphs .
III Creating Graphics: The Functions of Graphics, The
Characteristics of an Effective Graphic, Understanding the
Process of Creating Graphics, Using Color Effectively,
Choosing the Appropriate Kind of Graphic, Creating Effective
Graphics for Multicultural Reade rs.Researching Your
Subject: Understanding the Differences Between Academic
and WorkplaceResearch, Understanding the Research Process,
Conducting Secondary Research, Conducting Primary
Research, Research and Documentation: Literature
Reviews, Interviewing for Information, Documenting Sources,
Copyright, Paraphrasing, Questionnaires. Report
Components: Abstracts, Introductions, Tables of Contents,
Executive Summaries, Feasibility Reports, Investigative
Reports, Laboratory Reports, Test Reports, Trip Reports,
Trouble Reports 12 CO3
IV Writing Proposals: Understanding the Process of Writing
Proposals, The Logistics of Proposals, The ―Deliverables‖ of
Proposals, Persuasion and Proposals, Writing a Proposal, The
Structure of the Proposal . Writing Informational Reports:
Understanding the Process of Writing Informational Reports,
Writing Directives, Writing Field Reports, Writing Progress
and Status Reports, Writing Incident Reports, Writing Meeting
Minutes . Writing Recommendation Reports: Understanding
the Role of Recommendation Reports, Using a Problem -
Solving Model for Preparing Recommendation Reports,
Writing Recommendation Reports . Reviewing, Evaluating,
and Testing Documents and Websites: Understanding
Reviewing, Evaluating, and Testing, Reviewing Documents
and Websites, Conducting Usability Evaluations, Conducting
Usability Tests, Using Internet tools to check writing Quality,
Duplicate Content Detector, What is Plagiarism?, How to
avoid writing plagiarism content? Innovation management:
an introduction: The imp ortance of innovation, Models of
innovation, Innovation as a management process . Market
adoption and technology diffusion: Time lag between
innovation and useable product, Innovation and the market
,Innovation and market vision ,Analysing internet search da ta
to help adoption andforecasting sales ,Innovative new
products and consumption patterns, Crowdsourcing for new
product ideas, Frugal innovation and ideas from
everywhere,Innovation diffusion theories . 12 CO4
V Managing innovation within firms: Organisations and
innovation, The dilemma of innovation management,
Innovation dilemma in low technology sectors, Dynamic
capabilities, Managing uncertainty, Managing innovation
projects Operations and process innovation: Operations
management, The nature o f design and innovation in the
context of operations, Process design, Process design and
innovation 12 CO5

Page 38

37 Managing intellectual property: Intellectual property, Trade
secrets, An introduction to patents, Trademarks, Brand names,
Copyright Management of research a nd development: What
is research and development?, R&D management and the
industrial context, R&D investment and company success,
Classifying R&D, R&D management and its link with
business strategy, Strategic pressures on R&D, Which
business to support and how?, Allocation of funds to R&D,
Level of R&D expenditure Managing R&D
projects: Successful technology management, The changing
nature of R&D management, The acquisition of external
technology, Effective R&D management, The link with the
product innovation process, Evaluating R&D projects .

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Technical Communication
Mike Markel Bedford/St.
Martin's 11 2014
2. Innovation Management and
New Product Development Paul Trott Pearson 06 2017
3. Handbook of Technical
Writing
Gerald J.
Alred , Charles T.
Brusaw , Walter E.
Oliu Bedford/St.
Martin's 09 2008
4. Technical Writing 101: A
Real-World Guide to
Planning and Writing
Technical Content Alan S. Pringle and
Sarah S. O'Keefe scriptorium 03 2009
5. Innovation and
Entrepreneurship Peter Drucker Harper
Business 03 2009



Course Outcomes:
After completion of the course, a student should be able to:
CO1: Develop technical documents that meet the requirements with standard guidelines. Understanding the
essentials and hands -on learning about effective Website Development.
CO2: Write Better Quality Content Which Ranks faster at Search Engines. Build effective Social Media Pages.
CO3: Evaluate the essentials parameters of effective Social Media Pages.
CO4: Understand importance of innovation and entrepreneurship.
CO5: Analyze research and development projects.

Page 39

38





PSIT3P1: Project Documentation and
Viva
M. Sc (Information Technology) Semester – III
Course Name: Project Documentation and Viva Course Code: PSIT3P1
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- --

The learners are expected to develop a project beyond the undergraduate level. Normal web sites, web applications,
mobile apps are not expected. Preferably, the project should be from the elective chosen by the learner at the post
graduate level. In semester three. The learner is supposed to prepare the synop sis and documentation. The same
project has to be implemented in Semester IV.
More details about the project is given is Appendix 1.

Page 40

39




PSIT302a: Applied Artificial
Intelligence
M. Sc (Information Technology) Semester – III
Course Name: Applied Artificial Intelligence Course Code: PSIT302a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
- To explore the applied branches of artificial intelligence
- To enable the learner to understand applications of artificial intelligence
- To enable the student to solve the problem aligned with derived branches of artificial intelligence.

Unit Details Lectures Outcome
I Review of AI: History, foundation and Applications
Expert System and Applications: Phases in Building Expert System,
Expert System Architecture, Expert System versus Traditional Systems,
Rule based Expert Systems, Blackboard Systems, Truth Maintenance
System, Applicati on of Expert Systems, Shells and Tools 12 CO1
II Probability Theory: joint probability , conditional probability, Bayes’s
theorem, probabilities in rules and facts of rule based system, cumulative
probabilities, rule based system and Bayesian method
Fuzzy Sets and Fuzzy Logic: Fuzzy Sets, Fuzzy set operations, Types of
Member ship Functions, Multivalued Logic, Fuzzy Logic, Linguistic
variables and Hedges, Fuzzy propositions, inference rules for fuzzy
propositions, fuzzy systems, possibility theory and othe r enhancement to
Logic 12 CO2
III Machine Learning Paradigms: Machine Learning systems, supervised
and un -supervised learning, inductive learning, deductive learning,
clustering, support vector machines, cased based reasoning and learning.
Artificial Neur al Networks: Artificial Neural Networks, Single -Layer
feedforward networks, multi -layer feed -forward networks, radial basis
function networks, design issues of artificial neural networks and
recurrent networks 12 CO3
IV Evolutionary Computation: Soft computing, genetic algorithms, genetic
programming concepts, evolutionary programming, swarm intelligence,
ant colony paradigm, particle swarm optimization and applications of
evolutionary algorithms.
Intelligent Agents: Agents vs software programs, classification of agents,
working of an agent, single agent and multiagent systems, performance
evaluation, architecture, agent communication language, applications 12 CO4
V Advanced Knowledge Representation Techniques:
Conceptual dependency theory, scri pt structures, CYC theory, script
structure, CYC theory, case grammars, semantic web.
Natural Language Processing: 12 CO5

Page 41

40 Sentence Analysis phases, grammars and parsers, types of parsers,
semantic analysis, universal networking language, dictionary

Books and References:
Sr.
No. Title Author/s Publisher Edition Year
1. Artificial Intelligence Saroj Kaushik Cengage 1st 2019
2. Artificial Intelligence:
A Modern Approach A. Russel, Peter Norvig 1st
3. Artificial Intelligence Elaine
Rich,KevinKnight,Shivashankar
B. Nair Tata Mc -
Grawhill 3rd

M. Sc (Information Technology) Semester – III
Course Name: Artificial Intelligence Practical Course Code: PSIT3P2a
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- --

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcome s:

After completion of course the learner will:
CO1: be able to understand the fundamentals con cepts of expert system and its applications.
CO2: be able to use probability and concept of fuzzy sets for solving AI based problems.
CO3: be able to understand the applications of Machine Learning. The learner can also apply fuzzy system for
solving problems.
CO4: learner will be able to apply to understand the applications of genetic algorithms in different problems related
to artificial intelligence.
CO5: A learner can use knowledge representation techniques in natural language processing.

Page 42

41





PSIT303a: Machine Learning
M. Sc (Information Technology) Semester – III
Course Name: Machine Learning Course Code: PSIT303a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 Understanding Human learning aspects.
 Understanding primitives in learning process by computer.
 Understanding nature of problems solved with Machine Learning

Unit Details Lectures Outcome
I Introduction: Machine learning, Examples of Machine
Learning Problems, Structure of Learning, learning versus
Designing, Training versus Testing, Characteristics of Machine
learning tasks, Predictive and descriptive tasks, Machine
learning Models: Geometric Models, Logical Models,
Probabilistic Models. Features: Feature types, Feature
Construction and Tran sformation, Feature Selection. 12
CO1
II Classification and Regression: Classification: Binary
Classification - Assessing Classification performance, Class
probability Estimation Assessing class probability Estimates,
Multiclass Classification. Regression : Assessing performance of
Regression - Error measures, Overfitting - Catalysts for
Overfitting, Case study of Polynomial Regression. Theory of
Generalization: Effective number of hypothesis, Bounding the
Growth function, VC Dimensions, Regularization theory . 12 CO2
III Linear Models: Least Squares method, Multivariate Linear
Regression, Regularized Regression, Using Least Square
regression for Classification. Perceptron, Support Vector
Machines, Soft Margin SVM, Obtaining probabilities from
Linear classifier s, Kernel methods for non -Linearity. 12 CO2
CO3
IV Logic Based and Algebraic Model: Distance Based
Models: Neighbours and Examples, Nearest Neighbours
Classification, Distance based clustering -K means Algorithm,
Hierarchical clustering, Rule Based Models: Rule learning for
subgroup discovery, Association rule mining. Tree Based
Models: Decision Trees, Ranking and Probability estimation
Trees, Regression trees, Clustering Trees.

12 CO2
CO3
CO4
V Probabilistic Model:
Normal Distribution and Its Geometric Interpretations, Naïve
Bayes Classifier, Discriminative learning with Maximum 12 CO5

Page 43

42 likelihood, Probabilistic Models with Hidden variables:
Estimation -Maximization Methods, Gaussian Mixtures, and
Compression based Models.
Trends In Machine Learning : Model and S ymbols - Bagging
and Boosting, Multitask learning, Online learning and Sequence
Prediction, Data Streams and Active Learning, Deep Learning,
Reinforcement Learning.

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Machine Learning: The Art
and Science of Algorithms that
Make Sense of Data Peter Flach Cambridge
University
Press 2012
2. Introduction to Statistical
Machine Learning with
Applications in R Hastie, Tibshirani,
Friedman Springer 2nd 2012
3. Introduction to Machine
Learning EthemAlpaydin PHI 2nd 2013

M. Sc (Information Technology) Semester – III
Course Name: Machine Learning Practical Course Code: PSIT3P3a
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:
After completion of the course, a student should be able to:
CO1 : Understand the key issues in Machine Learning and its associated applications in intelligent business and
scientific computing.
CO2 : Acquire the knowledge about classification and regression techniques where a learner will be able to explore
his skill to generate data base knowledge using the prescribed techniques.
CO3 : Understand and implement the techniques for extracting the knowledge using machine learning methods.
CO4 : Achieve adequate perspectives of big data analytic s in various applications like recommender systems, social
media applications etc.
CO5 : Understand the statistical approach related to machine learning. He will also Apply the algorithms to a real -
world problem, optimize the models learned and report on th e expected accuracy that can be achieved by applying
the models.

Page 44

43








PSIT304a: Robotic Process
Automation
M. Sc (Information Technology) Semester – III
Course Name: Robotic Process Automation Course Code: PSIT304a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 To make the students aware about the automation today in the industry.
 To make the students aware about the tools used for automation.
 To help the students automate a complete process

Unit Details Lectures Outcome
I Robotic Process Automation: Scope and techniques of
automation, About UiPath
Record and Play: UiPath stack, Downloading and installing
UiPath Studio, Learning UiPath Studio, Task recorder, Step -
by-step examples using the recorder. 12 CO1
II Sequence, Flowchart, and Control Flow: Sequencing the
workflow, Activities, Control flow, various types of loops,
and decision making, Step -by-step example using Sequence
and Flowchart, Step -by-step example using Sequence and
Control flow
Data Manipulation: Variables and scope, Collections,
Arguments – Purpose and use, Data table usage with
examples, Clipboard management, File operation with step -
by-step example, CSV/Excel to data table and vice versa
(with a step -by-step example)
12 CO2
III Taking Control of the Controls : Finding and attachi ng
windows, Finding the control, Techniques for waiting for a
control, Act on controls – mouse and keyboard activities,
Working with UiExplorer, Handling events, Revisit recorder,
Screen Scraping, When to use OCR, Types of OCR
available, How to use OCR, Av oiding typical failure points
Tame that Application with Plugins and Extensions:
Terminal plugin, SAP automation, Java plugin, Citrix
automation, Mail plugin, PDF plugin, Web integration,
Excel and Word plugins, Credential management, 12 CO3

Page 45

44 Extensions – Java, Ch rome, Firefox, and Silverlight
IV Handling User Events and Assistant Bots: What are
assistant bots?, Monitoring system event triggers, Hotkey
trigger, Mouse trigger, System trigger ,Monitoring image
and element triggers, An example of monitoring email,
Example of monitoring a copying event and blocking it,
Launching an assistant bot on a keyboard event
Exception Handling, Debugging, and Logging: Exception
handling, Common exceptions and ways to handle them,
Logging and taking screenshots, Debuggin g techniques,
Collecting crash dumps, Error reporting 12 CO4
V Managing and Maintaining the Code: Project
organization, Nesting workflows, Reusability of workflows,
Commenting techniques, State Machine, When to use
Flowcharts, State Machines, or Sequence s, Using config files
and examples of a config file, Integrating a TFS server
Deploying and Maintaining the Bot: Publishing using
publish utility, Overview of Orchestration Server, Using
Orchestration Server to control bots, Using Orchestration
Server to deploy bots, License management, Publishing and
managing updates 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Learning Robotic Process
Automation Alok Mani Tripathi Packt 1st 2018
2. Robotic Process Automation
Tools, Process Automation
and their benefits:
Understanding RPA and
Intelligent Automation Srikanth Merianda Createspace
Independent
Publishing 1st 2018
3. The Simple Implementation
Guide to Robotic Process
Automation (Rpa): How to
Best Implement Rpa in an
Organization Kelly Wibbenmeyer iUniverse
1st 2018

M. Sc (Information Technology) Semester – III
Course Name: Robotic Process Automation Course Code: PSIT3P4a
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:

After completing the course, a learner will be able to:

CO1: Understand the mechanism of business process and can provide the solution in an optimize way.

Page 46

45 CO2: Understand the features use for interacting with database plugins.
CO3: Use the plug -ins and other controls used for process automation.
CO4: Use and handle the different events, debugging and managing the errors.
CO5: Test and deploy the automated process.

Page 47

46



SEMESTER IV

Page 48

47 PSIT401: Blockchain – Compulsory
Course
M. Sc ( Information Technology ) Semester – IV
Course Name: Blockchain Course Code: PSIT401
Periods per week (1 Period is 6 0 minutes ) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 To provide conceptual understanding of the function of Blockchain as a method of securing distributed
ledgers, how consensus on their contents is achieved, and the new applications that they enable.
 To cover the technological underpinnings of blockchain operations as distributed data structures and
decision -making systems, their functionality and different architecture types.
 To provide a critical evaluation of existing ―smart contract‖ capabilities and platforms, and examine their
future directions, opportunities, risks and challenges.

Unit Details Lectures Outcome
I Blockchain: Introduction, History, Centralised versus
Decentralised systems, Layers of blockchain, Importance of
blockchain, Blockchain uses and use cases.
Working of Blockchain: Blockchain foundation,
Cryptography, Game Theory, Computer Science
Engineering, Properties of blockchain solutions, blockchain
transactions, distributed consensus mechanisms, Blockchain
mechanisms, Scaling blockchain
Working of Bitcoin: Money, Bitcoin, Bitcoin blockchain,
bitcoin network, bitcoin scrip ts, Full Nodes and SVPs,
Bitcoin wallets. 12 CO1
II Ethereum: three parts of blockchain, Ether as currency and
commodity, Building trustless systems, Smart contracts,
Ethereum Virtual Machine, The Mist browser, Wallets as a
Computing Metaphor, The Bank Teller Metaphor, Breaking
with Banking History, How Encryption L eads to Trust,
System Requirements, Using Parity with Geth, Anonymity
in Cryptocurrency, Central Bank Network, Virtual
Machines, EVM Applications, State Machines, Guts of the
EVM, Blocks, Mining’s Place in the State Transition
Function, Renting Time on the EVM, Gas, Working with
Gas, Accounts, Transactions, and Messages, Transactions
and Messages, Estimating Gas Fees for Operations, Opcodes
in the EVM.
Solidity Programming: Introduction,Global Banking Made
Real, Complementary Currency, Programming the EVM,
Design Rationale, Importance of Formal Proofs, Automated
Proofs, Testing, Formatting Solidity Files, Reading Code,
Statements and Expressions in Solidity, Value Types, Global
Special Variables, Units, and Functions, 12 CO2
III Hyperledger: Overview, Fabric, composer, installing
hyperledger fabric and composer, deploying, running the 12 CO3

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48 network, error troubleshooting.
Smart Contracts and Tokens: EVM as Back End, Assets
Backed by Anything, Cryptocurrency Is a Measure of Time,
Function of Collecti bles in Human Systems, Platforms for
High -Value Digital Collectibles, Tokens as Category of
Smart Contract, Creating a Token, Deploying the Contract,
Playing with Contracts.
IV Mining Ether: Why? Ether’s Source, Defining Mining,
Difficulty, Self -Regulation, and the Race for Profit, How
Proof of Work Helps Regulate Block Time, DAG and
Nonce, Faster Blocks, Stale Blocks, Difficulties, Ancestry of
Blocks and Transactions, Ethereum and Bitcoin, Fo rking,
Mining, Geth on Windows, Executing Commands in the
EVM via the Geth Console, Launching Geth with Flags,
Mining on the Testnet, GPU Mining Rigs, Mining on a Pool
with Multiple GPUs.
Cryptoecnomics: Introduction, Usefulness of
cryptoeconomics, Speed o f blocks, Ether Issuance scheme,
Common Attack Scenarios. 12 CO4
V Blockchain Application Development: Decentralized
Applications, Blockchain Application Development,
Interacting with the Bitcoin Blockchain, Interacting
Programmatically with Ethereum —Sending Transactions,
Creating a Smart Contract, Executing Smart Contract
Functions, Public vs. Private Blockchains, Decentralized
Application Architecture, Building an Ethereum
DApp: The DApp, Setting Up a Private Ethereum Network,
Creating the Smart Contract , Deploying the Smart Contract,
Client Application, DApp deployment: Seven Ways to
Think About Smart Contracts, Dapp Contract Data Models,
EVM back -end and front -end communication, JSON -RPC,
Web 3, JavaScript API, Using Meteor with the EVM,
Executing Contr acts in the Console, Recommendations for
Prototyping, Third -Party Deployment Libraries, Creating
Private Chains. 12 CO5





Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Beginning Blockchain
A Beginner’s Guide to
Building Blockchain
Solutions Bikramaditya Singhal,
Gautam Dhameja,
Priyansu Sekhar Panda Apress 2018
2. Introducing Ethereum and
Solidity Chris Dannen Apress 2017
3. The Blockchain Developer EladElrom Apress 2019
4. Mastering Ethereum Andreas M.
Antonopoulos
Dr. Gavin Wood O’Reilly First 2018
5. Blockchain Enabled
Applications Vikram Dhillon
David Metcalf
Max Hooper Apress 2017

Page 50

49
M. Sc ( Information Technology ) Semester – III
Course Name: Blockchain Course Code: PSIT
Periods per week (1 Period is 6 0 minutes ) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.


Course Outcome s:
After completion of the course, a student should be able to:

CO1: The students would understand the structure of a blockchain and why/when it is better than a simple
distributed database.

CO2: Analyze the incentive structure in a blockchain based system and critically assess its functions, benefits and
vulnerabilities

CO3: Evaluate the setting where a blockchain based structure may be applied, its potential and its limitations

CO4: Understand what constitutes a ―smart‖ contract, wh at are its legal implications and what it can and cannot do,
now and in the near future

CO5: Develop blockchain DApps.

Page 51

50


PSIT402a: Natural Language
Processing
M. Sc (Information Technology) Semester – IV
Course Name: Natural Language Processing Course Code: PSIT402a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 The prime objective of this course is to introduce the students to the field of Language Computing
and its applications ranging from classical era to modern context.
 To provide understanding of various NLP tasks and NLP abstractions such as Morphological
analysis, POS tagging, concept of syntactic parsing, semantic analysis etc.
 To provide knowledge of different approaches/algorithms for carrying out NLP tasks.
 To highlight the concepts of Language grammar and grammar representation in Computational
Ling uistics.

Unit Details Lectures Outcome
I Introduction to NLP, brief history, NLP applications: Speech
to Text(STT), Text to Speech(TTS), Story Understanding,
NL Generation, QA system, Machine Translation, Text
Summarization, Text classification, Sentiment Analysis,
Grammar/Spell Checkers etc., ch allenges/Open Problems,
NLP abstraction levels, Natural Language (NL)
Characteristics and NL computing approaches/techniques
and steps, NL tasks: Segmentation, Chunking, tagging, NER,
Parsing, Word Sense Disambiguation, NL Generation, Web
2.0 Applications : Sentiment Analysis; Text Entailment;
Cross Lingual Information Retrieval (CLIR). 12 CO1
II Text Processing Challenges, Overview of Language Scripts
and their representation on Machines using Character Sets,
Language, Corpus and Application Dependence issues,
Segmentation: word level(Tokenization), Sentence level.
Regular Expression and Automata Morphology, Types,
Survey of English and Indian Languages Morphology,
Morphological parsing FSA and FST, Porter stemmer, Rule
based and Paradigm based Morphology, Human
Morphological Processing, Machine Learning approaches. 12 CO2
III Word Classes ad Par t-of-Speech tagging(POS), survey of
POS tagsets, Rule based approaches (ENGTOWL),
Stochastic approaches(Probabilistic, N -gram and HMM),
TBL morphology, unknown word handling, evaluation
metrics: Precision/Recall/F -measure, error analysis. 12 CO3
IV NL parsing basics, approaches: TopDown, BottomUp, 12 CO4

Page 52

51 Overview of Grammar Formalisms: constituency and
dependency school, Grammar notations CFG, LFG, PCFG,
LTAG, Feature - Unification, overview of English CFG,
Indian Language Parsing in Paninian Karaka Theory, CFG
parsing using Earley’s and CYK algorithms, Probabilistic
parsing, Dependency Parsing: Covington algorithm, MALT
parser, MST parser.
V Concepts and issues in NL, Theories and approaches for
Semantic Analysis, Meaning Representation, word
simila rity, Lexical Semantics, word senses and relationships,
WordNet (English and IndoWordnet), Word Sense
Disambiguation: Lesk Algorithm Walker’s algorithm,
Coreferences Resolution:Anaphora, Cataphora. 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Handbook of Natural
Language Processing Indurkhya , N.,
&Damerau,
F. J. CRC Press
Taylor and
Francis Group 2nd 2010
2. Speech and Language
Processing Martin, J. H.,
&Jurafsky,
D. Pearson
Education
India 2nd 2013
3. Foundations of Statistical
Natural Language Processing
Manning,
Christopher
and Heinrich,
Schutze MIT Press
1st 1997
4. Natural Language Processing
With Python
Steven Bird,
Edward
Loper O'Reilly
Media
2nd 2016
5. Video Links
1. http://www.nptelvideos.in/2012/11/natural -language -processing.html

M. Sc (Information Technology) Semester – IV
Course Name: Natural Language Processing Practical Course Code: PSIT4P2a
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:
After completion of the course, a student should be able to:
CO1 : Students will get idea about know -hows, issues and challenge in Natural Language Processing and
NLP applications and their relevance in the classical and modern context.

Page 53

52 CO2 : Student will get understanding of Computational techniques and approaches for solving NLP
problems and develop modules for NLP tasks and tools such as Morph Analyzer, POS tagger, Chunker,
Parser, WSD tool etc.
CO3 : Students will also be introduced to va rious grammar formalisms, which they can apply in different
fields of study.
CO4 : Students can take up project work or work in R&D firms working in NLP and its allied areas.

CO5 : Student will be able to understand applications in different sectors

Page 54

53 PSIT403a: Deep Learning
M. Sc (Information Technology) Semester – IV
Course Name: Deep Learning Course Code: PSIT403a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 To present the mathematical, statistical and computational challenges of building neural networks
 To study the concepts of deep learning
 To enable the students to know deep learning techniques to support real -time applications

Unit Details Lectures Outcome
I Applied Math and Machine Learning Basics: Linear
Algebra: Scalars, Vectors, Matrices and Tensors ,
Multiplying Matrices and Vectors , Identity and Inverse
Matrices, Linear Dependence and Span , norms, special
matrices and vectors, eigen decompositions.
Numerical Computation: Overflow and under flow, poor
conditioning, Gradient Based Optimization, Const raint
optimization. 12 CO1
II Deep Networks: Deep feedforward network , regularization
for deep learning , Optimization for Training deep models 12 CO2
III Convolutional Networks, Sequence Modelling, Applications 12 CO3
IV Deep Learning Research: Linear Factor Models,
Autoencoders, representation learning 12 CO4
V Approximate Inference, Deep Generative Models 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Deep Learning Ian Goodfellow,
YoshuaBengio, Aaron
Courvile An MIT
Press book 1st 2016
2. Fundamentals of Deep
Learning Nikhil Buduma O’Reilly 1st 2017
3. Deep Learning: Methods and
Applications Deng & Yu Now
Publishers 1st 2013
4. Deep Learning CookBook Douwe Osinga O’Reilly 1st 2017




M. Sc (Information Technology) Semester – IV
Course Name: Deep Learning Practical Course Code: PSIT4P3a
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

Page 55

54
List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:
After completion of the course, a student should be able to:
CO1 : Describes basics of mathematical foundat ion that will help the learner to understand the concepts of
Deep Learning.
CO2 : Understand and describe model of deep learning
CO3 : Design and implement various deep supervised learning architectures for text & image data.
CO4 : Design and implement various deep learning models and architectures.
CO5 : Apply various deep learning techniques to design efficient algorithms for real -world applications.

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55

PSIT404a: Human Computer
Interaction
M. Sc (Information Technology) Semester – IV
Course Name: Human Computer Interaction Course Code: PSIT404a
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 Understand the important aspects of implementation of human -computer interfaces.
 Identify the various tools and techniques for interface analysis, design, and evaluation.
 Identify the impact of usable interfaces in the acceptance and pe rformance utilization of information
systems

Unit Details Lectures Outcome
I The Interaction: Models of interaction, Design Focus,
Frameworks and HCI, Ergonomics, Interaction styles,
Elements of the WIMP interface,Interactivity
Paradigms: Introduction, Paradigms for interaction
Interaction design basics: What is design?, The process of
design, User focus, Cultural probes, Navigation design, the
big button trap, Modes, Screen design and layout, Alignment
and layout matters, Checking screen colors, Iteration and
prototyping
HCI in the software process : The software life cycle,
Usability engineering , Iterative design and prototyping,
Prototyping in practice, Design rationale 12 CO1
II Design : Principles to support usability, Standards,
Guidelines, Golden rules and heuristics, HCI patterns
Implementation support: Elements of windowing systems,
Programming the application, Going with the grain, Using
toolkits, User interface management systems
Evaluation techniques: What is evaluation?, Go als of
evaluation, Evaluation through expert analysis, Evaluation
through user participation, Choosing an evaluation method 12 CO2
III Universal design: Universal design principles, Multi -modal
interaction, Designing websites for screen readers, Choosing
the right kind of speech, Designing for diversity
User support: Requirements of user support, Approaches to
user support, Adaptive help systems, Designing user support
systems
Cognitive models: Goal and task hierarchies, Linguistic
models, The challenge of display -based systems, Physical
and device models, Cognitive architectures 12 CO3
IV Socio -organizational issues and stakeholder
requirements: Organizational issues, Capturing
requirements 12 CO4

Page 57

56 Communication and collaboration models: Face -to-face
communication, Conversation, Text -based communication,
Group working
Task analysis: Differences between task analysis and other
techniques, Task decomposition, Knowledge -based analysis,
Entity –relationship -based techniques, Sources of information
and data collection, Uses of task analysis
V Dialog notations and design: What is dialog?, Dialog
design notations, Diagrammatic notations, Textual dialog
notations, Dialog semantics, Dialog analysis and design
Models of the system: Standard formalisms, Int eraction
models, Continuous behavior
Modeling rich interaction: Status –event analysis, Rich
contexts, Low intention and sensor -based interaction 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Human Computer
Interaction Alan Dix, Janet Finlay,
Gregory Abowd,
Russell Beale Pearson
Education 3rd
2. Designing the User Interface Shneiderman B.,
Plaisant C., Cohen M.,
Jacobs S. Pearson 5th 2013


Course Outcomes:
After completion of the course, a student should be able to:
CO1: have a clear understanding of HCI principles that influence a system’s interface design, before writing any
code.
CO2: understand the evaluation techniques used for any of the proposed system.
CO3: understand the cognitive models and its design.
CO4: able to understand how to manage the system resources and do the task analysis.
CO5: able to design and implement a complete system.

PSIT4P4: Project Implementation and
Viva
M. Sc (Information Technology) Semester – IV
Course Name: Project Implementation and Viva Course Code: PSIT4P4
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

The project dissertation and Viva Voce details are given in Appendix 1.

Page 58

57 Evaluation Scheme
Internal Evaluation (40 Marks)
The internal assessment marks shall be awarded as follows:
1. 30 marks (Any one of the following):
a. Written Test or
b. SWAYAM (Advanced Course) of minimum 20 hours and certification exam completed or
c. NPTEL (Advanced Course) of minimum 20 hours and certification exam completed or
d. Valid International Certifications (Prometric, Pearson, Certiport, Coursera, Udemy and the
like)
e. One certification marks shall be awarded one course only. For four courses, the students will
have to complete four certifications.
2. 10 marks
The marks given out of 40 (30 in Semester 4) for publishing the research paper should be divided
into four course and should awarded out of 10 in each of the four course.

i. Suggeste d format of Question paper of 30 marks for the written test.
Q1. Attempt any two of the following: 16
a.
b.
c.
d.

Q2. Attempt any two of the following: 14
a.
b.
c.
d.

ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on publishing of
research paper in UGC approved / Other Journal with plagiarism less than 10%. The marks can be
awarded as per the impact factor of the journal, quality of the paper, importance of the contents
published, social value.


External Examination: (60 marks)

All questions are compulsory
Q1 (Based on Unit 1) Attempt any two of the following: 12
a.
b.
c.
d.

Q2 (Based on Unit 2) Attempt any two of the following: 12
Q3 (Based on Unit 3) Attempt any two of the following: 12
Q4 (Based on Unit 4) Attempt any two of the following: 12
Q5 (Based on Unit 5) Attempt any two of the following: 12

Practical Evaluation (50 marks)

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58
A Certified copy of hard -bound journal is essential to appear for the practical examination.

1. Practical Question 1 20
2. Practical Question 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5

Project Documentation and Viva Voce
Evaluation
The documentation should be checked for plagiarism and as per UGC guidelines, should be less than 10%.

1. Documentation Report (Chapter 1 to 4) 20
2. Innovation in the topic 10
3. Documentation/Topic presentation and viva voce 20

Project Implementation and Viva
Voce Evaluation
1. Documentation Report (Chapter 5 to last) 20
2. Implementation 10
3. Relevance of the topic 10
4. Viva Voce 10

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59

Appendix – 1
Project Documentation and Viva -voce (Semester III) and
Project Implementation and Viva -Voce (Semester IV)


Goals of the course Project Documentation and Viva -Voce

The student should:
 be able to apply relevant knowledge and abilities, within the main field of study, to a given problem
 within given constraints, even with limited information, independently analyse and discuss complex
inquiries/problems and handle larger problems on the a dvanced level within the main field of study
 reflect on, evaluate and critically review one’s own and others’ scientific results
 be able to document and present one’s own work with strict requirements on structure, format, and language
usage
 be able to ide ntify one’s need for further knowledge and continuously develop one’s own knowledge

To start the project:
 Start thinking early in the programme about suitable projects.
 Read the instructions for the project.
 Attend and listen to other student´s final oral presentations.
 Look at the finished reports.
 Talk to senior master students.
 Attend possible information events (workshops / seminars / conferences etc.) about the related topics.

Application and approva l:
 Read all the detailed information about project.
 Finalise finding a place and supervisor.
 Check with the coordinator about subject/project, place and supervisor.
 Write the project proposal and plan along with the supervisor.
 Fill out the application together with the supervisor.
 Hand over the complete application, proposal and plan to the coordinator.
 Get an acknowledgement and approval from the coordinator to start the project.


During the project :
 Search, gather and read information and literat ure about the theory.
 Document well the practical work and your results.
 Take part in seminars and the running follow -ups/supervision.
 Think early on about disposition and writing of the final report.
 Discuss your thoughts with the supervisor and others.
 Read the SOP and the rest you need again.
 Plan for and do the mid -term reporting to the coordinator/examiner.
 Do a mid -term report also at the work -place (can be a requirement in some work -places).
 Write the first draft of the final report and rewrite it based on feedback from the supervisor and possibly others.
 Plan for the final presentation of the report.

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60
Finishing the project :
 Finish the report and obtain an OK from the supervisor.
 Ask the sup ervisor to send the certificate and feedback form to the coordinator.
 Attend the pre -final oral presentation arranged by the Coordinator.
 Rewrite the final report again based on feedback from the opponents and possibly others.
 Prepare a title page and a p opular science summary for your report.
 Send the completed final report to the coordinator (via plagiarism software)
 Rewrite the report based on possible feedback from the coordinator.
 Appear for the final exam.

Project Proposal /research plan
 The studen t should spend the first 1 -2 weeks writing a 1 -2 pages project plan containing:
- Short background of the project
- Aims of the project
- Short description of methods that will be used
- Estimated time schedule for the project
 The research plan should be handed in to the supervisor and the coordinator.
 Writing the project plan will help you plan your project work and get you started in finding information and
understanding of methods needed to perform the project .

Project Docum entation
The documentation should contain:
 Introduction - that should contain a technical and social (when possible) motivation of the project topic .
 Description of the problems/topics.
 Status of the research/knowledge in the field and literature review.
 Description of the methodology/approach. (The actual structure of the chapters here depends on the topic of the
documentation .)
 Results - must always contain analyses of results and associated uncertainties.
 Conclusions and proposals for the future work.
 Appendices (when needed).
 Bibliography - references and links.

For the master’s documentation, the chapters cannot be dictated, they may vary according to the type of
project. However, in Semester III Project Documentation and Viva Voce must contain at lea st 4 chapters
(Introduction, Review of Literature, Methodology / Approach, Proposed Design / UI design, etc. depending
on the type of project.) The Semester III report should be spiral bound.

In Semester IV, the remaining Chapters should be included (which should include Experiments performed,
Results and discussion, Conclusions and proposals for future work, Appendices) and Bibliography -
references and links . Semester IV report should include all the chapters and should be hardbound.

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2


AC – 11/07/2022
ItemNo. 6.13 (2) (R)




































UNIVERSITY OF MUMBAI



Revised Syllabus for

M.Sc. IT (Security)

PartI I (Semester I to IV)
(Choice Based Credit System)




(With effect from the academic year 2022 -2023)

Page 63

3

Page 64

4
Semester –I
Course Code Course Title Credits
PSIT101 Researchin Computing 4
PSIT102 Data Science 4
PSIT103 Cloud Computing 4
PSIT104 SoftComputing Techniques 4
PSIT1P1 ResearchinComputing Practical 2
PSIT1P2 DataScience Practical 2
PSIT1P3 CloudComputing Practical 2
PSIT1P4 SoftComputingTechniques Practical 2
Total Credits 24


Semester –II
Course Code Course Title Credits
PSIT201 BigData Analytics 4
PSIT202 Modern Networking 4
PSIT203 Microservices Architecture 4
PSIT204 Image Processing 4
PSIT2P1 BigDataAnalytics Practical 2
PSIT2P2 ModernNetworking Practical 2
PSIT2P3 MicroservicesArchitecture Practical 2
PSIT2P4 ImageProcessing Practical 2
Total Credits 24

Page 65

5 ProgramSpecific Outcomes
PSO1: Ability to applythe knowledge ofInformation Technology with recent trendsalignedwith
research and industry.

PSO2: Ability to apply IT in the field of Computational Research, Soft Computing, Big Data
Analytics, Data Science, Image Processing, Artificial Intelligence, Networking and Cloud
Computing.

PSO3: Ability to provide socially acceptable technical solutions in the domains of Information
Security,MachineLearning,InternetofThingsandEmbeddedSystem,InfrastructureServicesas
specializations.

PSO4: Ability to apply the knowledge of Intellectual Property Rights, Cyber Laws and Cyber
Forensics and various standards in interest of National Security and Integrity along with IT
Industry.

PSO5: Ability to write effective project reports, research publications and content development
and to work in multidisciplinary environment in the context of changing technologies.

Page 66

6




















SEMESTER I

Page 67

7  Tobeabletoconductbusinessresearchwithanunderstandingofall the
latest theories.
 Todeveloptheabilitytoexploreresearchtechniquesusedforsolving any
real world or innovate problem. Objectives
Basicknowledgeof statisticalmethods.Analyticalandlogical thinking. Pre requisites M.Sc(Information Technology) Semester – I
CourseName: Researchin Computing CourseCode: PSIT101
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40





Unit Details Lectures
I Introduction: Role of Business Research, Information Systems and
Knowledge Management, Theory Building, Organization ethics and
Issues
12
II Beginning Stages of Research Process: Problem definition,
Qualitative research tools, Secondary data research 12
III Research Methods and Data Collection: Survey research,
communicatingwithrespondents,Observationmethods, Experimental
research
12
IV Measurement Concepts, Samplingand Field work: Levelsof Scale
measurement, attitude measurement, questionnaire design, sampling
designs and procedures, determination of sample size
12
V Data Analysis and Presentation: Editing and Coding, Basic Data
Analysis, Univariate Statistical Analysis and Bivariate Statistical
analysis and differences between two variables. Multivariate Statistical
Analysis.
12


Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. BusinessResearch Methods William
G.Zikmund, B.J
Babin,J.C. Carr, Cengage 8e 2016

Page 68

8
Alearnerwillbeable to:
solve real world problems with scientific approach.
developanalyticalskillsbyapplyingscientificmethods.
recognize,understandandapplythelanguage,theoryandmodelsof the
field of business analytics
fosteranabilityto criticallyanalyze,synthesizeandsolvecomplex
unstructured business problems
understandandcriticallyapplytheconceptsandmethodsof business
analytics
identify,modelandsolvedecisionproblemsindifferentsettings
interpret results/solutions and identify appropriate courses of
action for a given managerial situation whether a problem or an
opportunity
createviablesolutionstodecisionmaking problems
Course Outcome AtanuAdhikari,
M.Griffin
2. Business
Analytics Albright
Winston Cengage 5e 2015
3. ResearchMethods for
BusinessStudentsFifth
Edition Mark Saunders 2011
4. MultivariateData Analysis Hair Pearson 7e 2014

M.Sc(Information Technology) Semester – I
CourseName: ResearchinComputing Practical CourseCode: PSIT1P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hou
rs Marks
Evaluation System Practical Examination 2 40



Practical No Details
1 - 10 10Practicalbasedon abovesyllabus,covering entire syllabus

Page 69

9
Developindepthunderstandingofthekeytechnologiesindatascience and
business analytics: data mining, machine learning, visualization
techniques, predictive modeling, and statistics.
Practiceproblemanalysisanddecision -making.
Gain practical, hands -on experience with statistics programming
languages andbig data tools
throughcourseworkandappliedresearch experiences. Objectives
Basicunderstandingof statistics Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Data Science CourseCode: PSIT102
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Data Science Technology Stack: Rapid Information Factory
Ecosystem, Data Science Storage Tools, Data Lake, Data Vault, Data
WarehouseBusMatrix,DataScience ProcessingTools,Spark,Mesos,
Akka,Cassandra,Kafka,ElasticSearch,R,Scala,Python,MQTT,The
Future
Layered Framework: Definition of Data Science Framework, Cross -
Industry Standard Process for Data Mining (CRISP -DM),
Homogeneous Ontology for Recursive Uniform Schema, The Top
Layers of a Layered Framework, Layered Framework for High -Level
Data Science and Engineering
Business Layer: Business Layer, Engineering a Practical Business
Layer
Utility Layer: Basic Utility Design, Engineering a Practical Utility
Layer




12
II Three Management Layers: Operational Management Layer,
Processing -Stream Definition and Management, Audit, Balance, and
Control Layer, Balance, Control, Yoke Solution, Cause -and-Effect,
Analysis System, Functional Layer, Data Science Process
Retrieve Superstep : Data Lakes, Data Swamps, Training the Trainer
Model, Understanding the Business Dynamics of the Data Lake,
Actionable Business Knowledge from Data Lakes, Engineering a
Practical Retrieve Superstep, Connecting to Other Data Sources,

12
III AssessSuperstep: AssessSuperstep,Errors,AnalysisofData, Practical
Actions, Engineering a Practical Assess Superstep, 12

Page 70

10  Apply quantitative modeling and data analysis techniques to the
solution of real world business problems, communicate findings, and
effectively present results using data visualization techniques.
 Recognize and analyze ethical issues in businessrelated to intellectual
property, data security, integrity, and privacy. Course Outcome IV Process Superstep : Data Vault, Time -Person -Object -Location -Event
Data Vault, Data Science Process, Data Science,
Transform Superstep : Transform Superstep, Building a Data
Warehouse, Transforming with Data Science, Hypothesis Testing,
Overfitting and Underfitting, Precision -Recall, Cross -Validation Test.
12
V Transform Superstep: Univariate Analysis, Bivariate Analysis,
Multivariate Analysis, Linear Regression, Logistic Regression,
Clustering Techniques, ANOVA, Principal Component Analysis
(PCA),DecisionTrees,SupportVectorMachines,Networks,Clusters, and
Grids, Data Mining, Pattern Recognition, Machine Learning, Bagging
Data,Random Forests, Computer Vision (CV) , Natural Language
Processing (NLP), Neural Networks, TensorFlow.
Organize and Report Supersteps : Organize Superstep, Report
Superstep, Graphics, Pictures, Showing the Difference


12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. PracticalData Science AndreasFrançois
Vermeulen APress 2018
2. PrinciplesofData Science Sinan Ozdemir PACKT 2016
3. DataSciencefrom Scratch Joel Grus O’Reilly 2015
4. DataSciencefromScratch first
Principle in python Joel Grus Shroff
Publishers 2017
5. ExperimentalDesignin
Datasciencewith Least
Resources NCDas Shroff
Publishers 2018


M.Sc(Information Technology) Semester – I
CourseName: DataScience Practical CourseCode: PSIT1P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 71

11  Applyethicalpracticesineverydaybusinessactivitiesandmakewell -
reasoned ethical business and data management decisions.
 Demonstrateknowledgeofstatistical dataanalysistechniquesutilized in
business decision making.
 ApplyprinciplesofDataSciencetotheanalysisofbusiness problems.
 Usedataminingsoftwaretosolvereal -world problems.
 Employcuttingedgetoolsand technologiestoanalyzeBig Data.
 Applyalgorithmstobuildmachine intelligence.
 Demonstrateuseofteamwork,leadershipskills,decisionmakingand
organization theory.

Page 72

12
TolearnhowtouseCloudServices. To
implement Virtualization.
To implement Task Scheduling algorithms.
Apply Map-Reduceconcepttoapplications.
To build Private Cloud.
Broadlyeducatetoknowtheimpactofengineeringonlegaland
societal issues involved. Objectives M.Sc(Information Technology) Semester – I
CourseName:Cloud Computing CourseCode: PSIT103
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Cloud Computing: Introduction, Historical
developments, BuildingCloudComputingEnvironments, Principlesof
ParallelandDistributedComputing: ErasofComputing,Parallelv/s
distributed computing, Elements of Parallel Computing, Elements of
distributed computing, Technologies for distributed computing.
Virtualization: Introduction, Characteristics of virtualized
environments, Taxonomy of virtualization techniques, Virtualization
and cloud computing, Pros and cons of virtualization, Technology
examples.LogicalNetworkPerimeter,VirtualServer,Cloud Storage
Device,Cloudusagemon itor,Resourcereplication, Ready -made
environment.



12
II CloudComputingArchitecture: Introduction,Fundamentalconcepts
andmodels,Rolesandboundaries,CloudCharacteristics,Cloud Delivery
models, Cloud Deployment models, Economics of the cloud,
Open challenges. FundamentalCloudSecurity: Basics,Threat
agents,Cloudsecuritythreats,additionalconsiderations. Industrial
Platforms and New Developments: Amazon Web Services,Google
App Engine, Microsoft Azure.

12
III Specialized Cloud Mechanisms: Automated Scaling listener, Load
Balancer, SLA monitor, Pay -per-use monitor, Audit monitor, fail over
system, Hypervisor, Resource Centre, Multidevice broker, State
Management Database. Cloud Management Mechanisms: Remote
administration system, Resource Ma nagement System, SLA
Management System, Billing Management System, Cloud Security
Mechanisms: Encryption, Hashing, Digital Signature, PublicKey
Infrastructure(PKI),IdentityandAccessManagement(IAM), Single

12

Page 73

13 Sign-On(SSO),Cloud -Based SecurityGroups,HardenedVirtual Server
Images
IV Fundamental Cloud Architectures: Workload Distribution
Architecture, Resource Pooling Architecture, Dynamic Scalability
Architecture, Elastic Resource Capacity Architecture, Service Load
Balancing Architecture, Cloud Bursting Architecture, Elastic Disk
ProvisioningArchitecture,RedundantStorageArchitecture. Advanced
Cloud Architectures: Hypervisor Clustering Architecture, Load
Balanced Virtual Server Instances Architecture, Non -Disruptive
Service Relo cation Architecture, Zero Downtime Architecture, Cloud
Balancing Architecture, Resource Reservation Architecture, Dynamic
Failure DetectionandRecoveryArchitecture,Bare -Metal Provisioning
Architecture,RapidProvisioningArchitecture,StorageWorkload
Management Architecture



12
V CloudDelivery ModelConsiderations: CloudDeliveryModels:The
CloudProviderPerspective,CloudDeliveryModels:TheCloud Consumer
Perspective, Cost Metrics and Pricing Models : Business Cost
Metrics, Cloud Usage Cost Metrics, Cost Management
Considerations, Service Quality Metrics and SLAs: Service Quality
Metrics, SLA Guidelines

12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Mastering
Cloud ComputingFoundatio
nsand Applications
Programming RajkumarBuyya,
Christian
Vecchiola, S.
Thamarai Selvi Elsevier - 2013
2. Cloud Computing
Concepts,Technology& Arc
hitecture ThomasErl,
Zaigham
Mahmood,
andRicardo
Puttini Prentice
Hall - 2013
3. Distributed and Cloud
Computing, From Parallel
ProcessingtotheInternetof
Things Kai Hwang, Jack
Dongarra,Geoffrey
Fox MK
Publishers -- 2012

Page 74

14  AnalyzetheCloudcomputingsetupwithitsvulnerabilitiesand
applications using different architectures.
 Designdifferentworkflowsaccordingtorequirementsandapply
map reduce programming model.
 ApplyanddesignsuitableVirtualizationconcept,CloudResource
Management and design scheduling algorithms.
 Createcombinatorialauct ionsforcloudresourcesanddesign
scheduling algorithms for computing clouds
 AssesscloudStoragesystemsandCloudsecurity,therisks
involved, its impact and develop cloud application
 Broadly educate to know the impact of engineering on legal and
societalissuesinv olvedinaddressingthesecurityissues ofcloud
computing. Course Outcome M.Sc(Information Technology) Semester – I
CourseName:CloudComputing Practical CourseCode: PSIT1P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 75

15 problem Allthesetechniqueswillbemoreeffectivetosolvethe efficiently • • Softcomputingconceptslikefuzzylogic,neuralnetworksandgenetic
algorithm, where Artificial Intelligence is mother branch of all. Objectives
BasicconceptsofArtificialIntelligence.Knowledgeof Algorithms Pre requisites M.Sc(Information Technology) Semester – I
CourseName:SoftComputing Techniques CourseCode: PSIT104
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction of soft computing, soft computing vs. hard computing,
various types of soft computing techniques, Fuzzy Computing, Neural
Computing, Genetic Algorithms, Associative Memory, Adaptive
Resonance Theory, Classification, Clustering, Bayesian Networks,
Probabilistic reasoning, applications of soft computing.

12
II Artificial Neural Network: Fundamental concept, Evolution of Neural
Networks, Basic Models, McCulloh -Pitts Neuron, Linear Separability,
Hebb Network.
Supervised Learning Network: Perceptron Networks, Adaptive Linear
Neuron,MultipleAdaptiveLinearNeurons,BackpropagationNetwork,
Radial BasisFunction,TimeDelayNetwork,FunctionalLinkNetworks,
Tree Neural Network.
Associative Memory Networks: Training algorithm for pattern
Association, Autoassociative memory network, hetroassociative
memory network, bi -directional associative memory, Hopfield
networks, iterative autoassociative memory networks, temporal
associative memory networks.



12
III UnSupervised Learning Networks: Fixed weight competitive nets,
Kohonen self -organizing feature maps, learning vectors quantization,
counter propogation networks, adaptive resonance theory networks.
Special Networks: Simulated annealing, Boltzman machine, Gaussian
Mach ine,CauchyMachine,Probabilisticneuralnet,cascadecorrelation
network, cognition network, neo -cognition network, cellular neural
network, optical neural network
ThirdGenerationNeural Networks:
SpikingNeuralnetworks,convolutionalneuralnetworks,deeplearning
neural networks, extreme learning machine model.



12

Page 76

16 IV IntroductiontoFuzzyLogic,ClassicalSetsandFuzzysets: Classical
sets, Fuzzy sets.
ClassicalRelationsandFuzzy Relations:
CartesianProductofrelation,classicalrelation,fuzzyrelations,
tolerance and equivalence relations, non -iterative fuzzy sets.
Membership Function: features of the membership functions,
fuzzification, methods of membership value assignments.
Defuzzification:Lambda -cutsforfuzzysets,Lambda -cutsforfuzzy
relations, Defuzzification methods.
FuzzyArithmeticandFuzzymeasures:fuzzyarithmetic,fuzzy
measures, measures of fuzziness, fuzzy integrals.



12
V FuzzyRulebaseandApproximate reasoning:
Fuzzy proportion, formation of rules, decomposition of rules,
aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems,
Fuzzy logic control systems, control system design, architecture and
operation of FLC system, FLC system models and applicat ions of FLC
System.
Genetic Algorithm: Biological Background, Traditional optimization
and search techniques, genetic algorithm and search space, genetic
algorithmvs.traditionalalgorithms,basicterminologies,simplegenetic
algorithm, general genetic algorith m, operators in genetic algorithm,
stopping condition for genetic algorithm flow, constraints in genetic
algorithm, problem solving using genetic algorithm, the schema
theorem,classificationofgeneticalgorithm,Hollandclassifiersystems,
genetic programming, advantages and limitations and applications of
genetic algorithm.
Differential Evolution Algorithm, Hybrid soft computing techniques –
neuro –fuzzyhybrid,geneticneuro -hybridsystems,genetic fuzzy
hybrid and fuzzy genetichybrid systems.





12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. ArtificialIntelligenceand Soft
Computing Anandita Das
Battacharya SPD 3rd 2018
2. PrinciplesofSoft computing S.N.SivanandamS
.N.Deepa Wiley 3rd 2019
3. Neuro -Fuzzy and Soft
Computing J.S.R.Jang,
C.T.Sunand
E.Mizutani Prentice
Hall of
India 2004
4. NeuralNetworks,Fuzzy
Logic and Genetic
Algorithms:Synthesis& Appli
cations S.Rajasekaran,
G.A.
Vijayalakshami Prentice
Hall of
India 2004
5. Fuzzy Logic with
EngineeringApplications Timothy J.Ross McGraw -
Hill 1997

Page 77

17 • Identifyanddescribesoftcomputingtechniquesandtheirrolesin
building intelligent machines
• Recognizethefeasibilityofapplyingasoftcomputingmethodology for
a particular problem
• Applyfuzzylogicandreasoningtohandleuncertaintyandsolve
engineering problems
• Applygeneticalgorithmstocombinatorialoptimization problems
• Applyneuralnetworksforclassificationandregression problems
• Effectivelyuseexistingsoftwaretoolstosolverealproblemsusing a
soft computing approach
• Evaluateandcomparesolutionsby varioussoftcomputing
approaches for a given problem. Course Outcome 6. GeneticAlgorithms: Search,
OptimizationandMachine
Learning Davis
E.Goldberg Addison
Wesley 1989
7. IntroductiontoAIand
Expert System Dan W.
Patterson Prentice
Hallof
India 2009

M.Sc(Information Technology) Semester – I
CourseName:SoftComputingTechniques Practical CourseCode: PSIT1P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus, coveringentire syllabus

Page 78

18

















SEMESTER II

Page 79

19  Toprovideanoverviewofanexcitinggrowingfieldofbigdata analytics.
 Tointroducethetoolsrequiredtomanageandanalyzebigdatalike
Hadoop, NoSql MapReduce.
 Toteachthefundamentaltechniquesandprinciplesinachievingbigdata
analytics with scalability and streaming capability.
 Toenablestudentstohaveskillsthatwillhelpthemtosolvecomplexreal - world
problems in for decision support. Objectives M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics CourseCode: PSIT201
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Big Data, Characteristics of Data, and Big Data
Evolution of Big Data, Definition of Big Data, Challenges with big
data, Why Big data? Data Warehouse environment, Traditional
Business Intelligence versus Big Data. State of Practice in Analy tics,
Key roles for New Big Data Ecosystems, Examples of big Data
Analytics.
BigDataAnalytics,Introductiontobigdataanalytics,Classificationof
Analytics, Challenges of Big Data, Importance of Big Data, Big Data
Technologies, Data Science, Responsibilities, Soft state eventual
consistency. Data Analytics Life Cycle


12
II Analytical Theory and Methods: Clustering and Associated
Algorithms, Association Rules, Apriori Algorithm, Candidate Rules,
ApplicationsofAssociationRules,Validationand Testing,
Diagnostics,Regression,LinearRegression,LogisticRegression,
Additional Regression Models.
12
III Analytical Theory and Methods: Classification, Decision Trees, Naïve
Bayes, Diagnostics of Classifiers, Additional Classification Methods,
TimeSeries Analysis,BoxJenkinsmethodology,ARIMAModel,
Additionalmethods.TextAnalysis,Steps,TextAnalysisExample,
Collecting Raw Text, Representing Text, Term Frequency -Inverse
DocumentFrequency(TFIDF),CategorizingDocumentsbyTopics,
Determining Sentiments

12
IV Data Product, Building Data Products at Scale with Hadoop, Data
Science Pipeline and Hadoop Ecosystem, Operating System for Big
Data, Concepts, Hadoop Architecture, Working with Distributed file
system,WorkingwithDistributedComputation,Frameworkfor Python
andHadoopStreaming,HadoopStreaming,MapReducewith Python,
12

Page 80

20 applications etc. Understand the key issues in big data management and its
associated applications in intelligent business and scientific
computing.
Acquire fundamental enabling techniques and scalable
algorithms like Hadoop, Map Reduce and NO SQL in bi g data
analytics.
Interpret business models and scientific computing paradigms,
and apply software tools for big data analytics.
Achieve adequate perspectives of big data analytics in various
applicationslikerecommendersystems,social media •


•


•

• Course Outcome Advanced MapReduce. In -Memory Computing with Spark, Spark
Basics,InteractiveSparkwithPySpark,WritingSparkApplications,
V Distributed Analysis and Patterns, Computing with Keys, Design
Patterns, Last -Mile Analytics, Data Mining and Warehousing,
StructuredDataQuerieswithHive,HBase,DataIngestion, Importing
RelationaldatawithSqoop,Injestingstreamdatawithflume.Analytics
with higher level APIs, Pig, Spark’s higher level APIs.
12
,
Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Big Data and Analytics Subhashini
Chellap panSee
maAcharya Wiley First
2. DataAnalyticswith Hadoop
AnIntroductionforData
Scientists Benjamin
Bengfortand
Jenny Kim O’Reilly 2016
3. Big Data and Hadoop V.KJain Khanna
Publishing First 2018

M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics Practical CourseCode: PSIT2P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 81

21  Tounderstandthestate -of-the-artinnetworkprotocols,architecturesand
applications.
 Analyzeexistingnetworkprotocolsand networks.
 Developnewprotocolsin networking
 Tounderstandhownetworkingresearchis done
 ToinvestigatenovelideasintheareaofNetworkingvia term-longresearch
projects. Objectives
Fundamentalsof Networking Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Modern Networking CourseCode: PSIT202
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Modern Networking
ElementsofModern Networking
The Networking Ecosystem ,Example Network Architectures,Global
Network Architecture,A Typical Network Hierarchy Ethernet
Applications of Ethernet Standards Ethernet Data Rates Wi -Fi
ApplicationsofWi -Fi,StandardsWi -FiDataRates4G/5GCellularFirst
Generation Second Generation, Third Generation Fourth Generation
Fifth Generation, Cloud Computin g Cloud Computing Concepts The
Benefits of Cloud Computing Cloud Networking Cloud Storage,
Internet of Things Things on the Internet of Things, Evolution Layers
of the Internet of Things, Network Convergence Unified
Communications, Requirements and Technol ogy Types of Network
andInternetTraffic,ElasticTraffic,InelasticTraffic,Real -TimeTraffic
Characteristics Demand: Big Data, Cloud Computing, and Mobile
TrafficBig Data Cloud Computing,,Mobile Traffic, Requirements:
QoS and QoE,,Quality of Service,Quality of Experience, Routing
Characteristics, Packet Forwarding, Congestion Control ,Effects of
Congestion,CongestionControlTechniques,SDNandNFV Software -
DefinedNetworking,NetworkFunctionsVirtualizationModern
Networking Elements







12
II Software -Defined Networks
SDN: Background and Motivation, Evolving Network Requirements
Demand Is Increasing,Supply Is IncreasingTraffic Patterns Are More
ComplexTraditionalNetworkArchitecturesareInadequate,The SDN
ApproachRequirementsSDNArchitectureCharacteristicsof Software -

12

Page 82

22 Defined Networking, SDN - and NFV -Related Standards Standards -
Developing Organizations Industry Consortia Open Development
Initiatives, SDN Data Plane and OpenFlow SDN Data Plane, Data
Plane Functions Data Plane Protocols OpenFlow Logical Network
DeviceFlowTableStructureFlowTablePipeline,TheUseofMultiple
Tables Group Table OpenFlow Protocol, SDN Control Plane
SDNControlPlaneArchitectureControlPlaneFunctions,Southbound
Interface Northbound InterfaceRouting, ITU -T Model,
OpenDaylightOpenDaylightArchitectureOpenDaylightHelium,RESTR
ESTConstraintsExampleRESTAPI,CooperationandCoordination
Among Controllers, Centralized Versus DistributedControllers, High -
AvailabilityClustersFederatedSDNNetworks,BorderGateway
ProtocolRoutingan dQoSBetweenDomains,UsingBGPforQoS
ManagementIETFSDNiOpenDaylightSNDi SDNApplication Plane
SDNApplicationPlaneArchitectureNorthboundInterfaceNetwork
ServicesAbstractionLayerNetworkApplications,UserInterface,
NetworkServicesAbstractionLayerAbstractionsinSDN, Frenetic Traffic
Engineering PolicyCop Measurement and Monitoring Security
OpenDaylight DDoS Application Data Center Networking, Big Data
overSDNCloudNetworkingoverSDNMobilityandWireless
Information -Centric Networking CCNx, Use of an Abstraction Layer
III Virtualization, Network Functions Virtualization: Concepts and
Architecture, Background and Motivation for NFV, Virtual Machines
The Virtual Machine Monitor, Architectural Approaches Container
Virtualization, NFV Concepts Simple Example of the Use of NFV,
NFV Principles High -Level NFV Framework, NFV Benefits and
RequirementsNFVBenefits,NFVRequirements, NFV Reference
Architecture NFV Management and Orchestration, Reference Points
Implementation, NFV Functionality, NFV
Infrastructure,ContainerInterface ,Deployment of NFVI
Containers,Logical Structure of NFVI Domains,ComputeDomain,
Hypervisor Domain,Infrastructure Network
Domain, Virtualized Network Functions, VNF
Interfaces,VNFC to VNFC Communication,VNF Scaling, NFV
Management and Orchestration, Virtualized Infrastructure
Manager,Virtual Network Function Manager,NFV Orchestrator,
Repositories, Element Management, OSS/BSS, NFV Use Cases
Architectural Use Cases, Service -Oriented Use Cases, SDN and NFV
Network Virtualization, Virtual LANs ,The Use of Virtual
LANs,Defining VLANs, Communicating VLAN Membership,IEEE
802.1Q VLAN Standard, Nested VLANs, OpenFlow VLAN Support,
VirtualPrivateNetworks, IPsec VPNs,MPLS VPNs, Network
Virtualization, Simplified Example, Network Virtualization
Architecture, Benefits of Network Virtualization, OpenDaylight’s
Virtual Tenant Network, Software -Defined Infrastructure,Software -
DefinedStorage,SDI Architecture









12

Page 83

23 IV DefiningandSupportingUserNeeds,QualityofService,Background,
QoSArchitectural Framework,DataPlane,Control Plane,Management
Plane, Integrated Services Architecture, ISA Approach
ISA Components, ISA Services, Queuing Discipline, Differentiated
Services, Services, DiffServ Field, DiffServ Configuration and
Operation,Pe r-HopBehavior,DefaultForwardingPHB,ServiceLevel
Agreements, IP Performance Metrics, OpenFlow QoS Support, Queue
Structures, Meters, QoE: User Quality of Experience, Why
QoE?,Online Video Content Delivery, Service Failures Due to
InadequateQoEConsiderations QoE-RelatedStandardizationProjects,
Definition of Quality of Experience, Definition of Quality, Definition
of Experience Quality Formation Process, Definition of Quality of
Experience, QoE Strategies in Practice, The QoE/QoS Layered Model
Summarizing and M erging the ,QoE/QoS Layers, Factors Influencing
QoE, Measurements of QoE, Subjective Assessment, Objective
Assessment, End -User Device Analytics, Summarizing the QoE
Measurement Methods, Applications of QoE Network Design
Implications of QoS and QoE Classi fication of QoE/ QoS Mapping
Models, Black -Box Media -Based QoS/QoE Mapping Models, Glass -
BoxParameter -BasedQoS/QoEMappingModels,Gray -BoxQoS/QoE
Mapping Models, Tips for QoS/QoE Mapping Model Selection,IP -
Oriented Parameter -Based QoS/QoE Mapping Models,Ne twork Layer
QoE/QoS Mapping Models for Video Services, Application Layer
QoE/QoS Mapping Models for Video Services Actionable QoE over
IP-Based Networks, The System -Oriented Actionable QoE Solution,
The Service -Oriented Actionable QoE Solution, QoE Versus QoS
Service Monitoring, QoS Monitoring Solutions, QoE Monitoring
Solutions,QoE -BasedNetworkandServiceManagement,QoE -Based
Management of VoIP Calls, QoE -Based Host -Centric Vertical
Handover, QoE -Based Network -Centric Vertical Handover












12
V Modern Network Architecture: Clouds and Fog, Cloud Computing,
Basic Concepts, Cloud Services, Software as a Service, Platform as a
Service,InfrastructureasaService,OtherCloudServices,XaaS,Cloud
Deployment Models, Public Cloud Private Cloud Community Cloud,
Hybrid Cloud, Cloud Architecture, NIST Cloud Computing Reference
Architecture,ITU -T Cloud Computing Reference Architecture, SDN and
NFV, Service Provider Perspective Private Cloud Perspective, ITU -T
Cloud Computing Functional Reference Architecture, The I nternet of
Things: Components The IoT Era Begins, The Scope of the Internet of
Things Components of IoT -Enabled Things, Sensors, Actuators,
Microcontrollers, Transceivers, RFID, The Internet of Things:
Architecture and Implementation, IoT Architecture,ITU -T IoT
Reference Model, IoT World Forum Reference Model, IoT
Implementation, IoTivity, Cisco IoT System, ioBridge, Security
Security Requirements, SDN Security Threats to SDN, Software -
Defined Security, NFV Security, Attack Surfaces, ETSI Security
Perspect ive,SecurityTechniques,CloudSecurity,SecurityIssues and
Concerns,CloudSecurityRisksandCountermeasures,Data Protection






12

Page 84

24  Demonstratein -depth knowledgeintheareaofComputer Networking.
 To demonstratescholarship of knowledge through performing in agroup
to identify, formulate and solve a problem related to ComputerNetworks
 Prepare a technical document for the identified Networking System
Conducting experiments to analyze the identified research work in
building Computer Networks Course Outcome intheCloud,Cloud SecurityasaService,AddressingCloudComputer
SecurityConcerns,IoTSecurity,ThePatching Vulnerability, IoT
SecurityandPrivacyRequirementsDefinedbyITU -TAnIoT Security
Framework, Conclusion

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Foundationsof Modern
Networking:SDN,NFV,
QoE, IoT, and Cloud William
Stallings Addison -
Wesley
Professional October
2015
2. SDNandNFVSimplified A
Visual Guide to
Understanding Software
Defined Networks and
NetworkFunction
Virtualization Jim Doherty Pearson
Education,
Inc
3. Network Functions
Virtualization(NFV)
withaTouchof SDN Rajendra
Chayapathi
SyedFarrukh
Hassan Addison -
Wesley
4. CCIEandCCDEEvolving
Technologies Study
Guide Braddgeworth,
Jason Gooley,
RamiroGarza
Rios Pearson
Education,
Inc 2019

M.Sc(Information Technology) Semester – II
CourseName:ModernNetworking Practical CourseCode: PSIT2P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 85

25
Gainathoroughunderstandingofthephilosophyandarchitectureof Web
applications using ASP.NET Core MVC;
Gainapracticalunderstandingof.NET Core;
AcquireaworkingknowledgeofWebapplicationdevelopmentusing
ASP.NET Core MVC 6 and Visual Studio
PersistdatawithXMLSerializationandADO.NETwithSQLServer Create
HTTP services using ASP.NET Core Web API;
DeployASP.NETCoreMVCapplicationstotheWindows Azure
cloud. Objectives M.Sc(Information Technology) Semester – I
CourseName:Microservice Architecture CourseCode: PSIT203
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Microservices: Understanding Microservices, Adopting
Microservices, The Microservices Way. Microservices Value
Proposition: DerivingBusinessValue,definingaGoal -
Oriented,LayeredApproach,Applyingthe Goal -
Oriented,LayeredApproach. Designing Microservice Systems:
The Systems Approach to Microservices, A
Microservices Design Process, Establishing a Foundation: Goals
and Principles, Platforms, Culture.

12
II Service Design: Microservice Boundaries, API design for
Microservices, Data and Microservices, Distributed Transactions and
Sagas, Asynchronous Message -Passing and Microservices, dealing
with Dependencies, System Design and Operations: Independent
Deployability, More Serv ers, Docker and Microservices, Role of
ServiceDiscovery,NeedforanAPIGateway,MonitoringandAlerting.
AdoptingMicroservicesinPractice: SolutionArchitecture Guidance,
OrganizationalGuidance,CultureGuidance,ToolsandProcess
Guidance, Services Guidance.


12
III Building Microservices with ASP.NET Core: Introduction,
Installing .NET Core, Building a Console App, Building ASP.NET
Core App. Delivering Continuously: Introduction to Docker,
Continuous integration with Wercker, Continuous Integration with
Circle CI, De ploying to Dicker Hub. Building Microservice with
ASP.NETCore: Microservice,TeamService,APIFirstDevelopment,
Test First Controller, Creating a CI pipeline, Integration Testing,
RunningtheteamserviceDockerImage. Backing Services:


12

Page 86

26 MicroservicesEcosystems,BuildingthelocationService,Enhancing
Team Service.
IV Creating Data Service: Choosing a Data Store , Building a Postgres
Repository, Databases are Backing Services, Integration Testing Real
Repositories, Exercise the Data Service. Event Sourcing and CQRS:
Event Sourcing, CQRS pattern, Event Sourcing and CQRS, Running
the samples. Building an ASP.NET Core Web Application:
ASP.NET Core Basics, Building Cloud -Native Web Applications.
ServiceDiscovery: CloudNativeFactors,NetflixEureka, Discovering
and Advertising ASP.NET Core Services. DNS and Platform Supported
Discovery.


12
V Configuring Microservice Ecosystems: Using Environment
VariableswithDocker,UsingSpringCloudConfigServer,Configuring
Microservices with etcd, Securing Applications and Microservices:
Security in the Cloud, Securing ASP.NET Core Web Apps, Securing
ASP.NET Core Microservices. Building Real -Time Apps and
Services: Real-Time Applications Defined, Websockets in the Cloud,
Using a Cloud Messaging Prov ider, Building the Proximity Monitor.
Putting It All Together: Identifying and Fixing Anti -Patterns,
Continuing the Debate over Composite Microservices, The Future.


12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Microservice Architecture:
Aligning Principles,
Practices, and Culture Irakli
Nadareishvili,
Ronnie Mitra,
MattMcLarty,
and Mike
Amundsen O’Reilly First 2016
2. BuildingMicroserviceswith
ASP.NET Core Kevin Hoffman O’Reilly First 2017
3. Building Microservices:
DesigningFine -Grained
Systems SamNewman O’Reilly First
4. Production -ready
Microservices SusanJ. Fowler O’Reilly 2016

Page 87

27
DevelopwebapplicationsusingModelView Control.
CreateMVCModelsandwritecodethatimplementsbusinesslogic
within Model methods, properties, and events.
CreateViewsinanMVCapplicationthatdisplay andeditdataand interact
with Models and Controllers.
Boostyourhireabilitythroughinnovativeandindependent learning.
Gaining a thorough understanding of the philosophy and
architecture of .NET Core
Understanding packages, metapackages and frameworks
Acquirin gaworkingknowledgeofthe.NETprogrammingmodel
Implementing multi -threading effectively in .NET applications Course Outcome M.Sc (Information Technology) Semester – II
CourseName:MicroservicesArchitecture Practical CourseCode: PSIT2P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 88

28  Reviewthefundamentalconceptsofadigitalimageprocessing
system.
 Analyzeimagesinthefrequencydomainusingvarious transforms.
 Evaluatethetechniques forimageenhancementandimage restoration.
 Categorizevariouscompression techniques.
 InterpretImagecompression standards.
 Interpretimagesegmentationandrepresentation techniques. Objectives M.Sc(Information Technology) Semester – II
CourseName: Image Processing CourseCode: PSIT204
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40



Unit Details Lectures
I Introduction: DigitalImageProcessing, OriginsofDigitalImageProcessing,
Applications and Examples of Digital Image Processing, Fundamental Steps
in Digital Image Processing, Components of an Image Processing System,
DigitalImageFundamentals: ElementsofVisualPerception,Lightandthe
Electromagneti c Spectrum, Image Sensing andAcquisition, Image Sampling
and Quantization, Basic Relationships Between Pixels, Basic Mathematical
Tools Used in Digital Image Processing, Intensity Transformations and
Spatial Filtering: Basics, Basic Intensity Transformatio n Functions, Basic
Intensity Transformation Functions, Histogram Processing, Fundamentals of
Spatial Filtering, Smoothing (Lowpass) Spatial Filters, Sharpening
(Highpass)SpatialFilters,Highpass,Bandreject,andBandpassFilters from
LowpassFilters,CombiningSpat ialEnhancementMethods,UsingFuzzy
Techniques for Intensity Transformations and Spatial Filtering




12
II Filtering in the Frequency Domain: Background, Preliminary Concepts,
Sampling and the Fourier Transform of Sampled Functions, The Discrete
Fourier Transform of One Variable, Extensions to Functions of Two
Variables, Properties of the 2 -D DFT and IDFT, Basics of Filtering in the
Frequency Do main, Image Smoothing Using Lowpass Frequency Domain
Filters, Image Sharpening Using Highpass Filters, Selective Filtering, Fast
Fourier Transform
Image Restoration and Reconstruction: A Model of the Image
Degradation/Restoration Process, Noise Models, Restoration in the Presence
of Noise Only -----Spatial Filtering, Periodic Noise Reduction Using
Frequency Domain Filtering, Linear, Position -Invariant Degradations,
EstimatingtheDegradationFunction,InverseFiltering,Minimum Mean
SquareError(Wiener)Filtering, ConstrainedLeastSquaresFiltering,
Geometric Mean Filter, Image Reconstruction from Projections




12
III WaveletandOtherImageTransforms: Preliminaries,Matrix -based
Transforms,Correlation,BasisFunctionsintheTime -FrequencyPlane, Basis 12

Page 89

29 Images, Fourier -Related Transforms, Walsh -Hadamard Transforms, Slant
Transform, Haar Transform, Wavelet Transforms
Color Image Processing: Color Fundamentals, Color Models, Pseudocolor
Image Processing, Full -Color Image Processing, Color Transformations,
ColorImageSmoothingandSharpening,UsingColorinImageSegmentation,
Noise in Color Images, Color Image Compression.
ImageCompressionandWatermarking: Fundamentals,HuffmanCoding,
GolombCoding,ArithmeticCoding,LZWCoding,Run -length Coding,
Symbol -basedCoding,8 Bit-planeCoding,BlockTransformCoding,
Predictive Coding, Wavelet Coding, Digital Image Watermarking,
IV Morphological Image Processing: Preliminaries, Erosion and Dilation,
Opening and Closing, The Hit -or-Miss Transform, Morphological
Algorithms, Morphol ogical Reconstruction¸ Morphological Operations on
Binary Images, Grayscale Morphology
ImageSegmentationI:EdgeDetection,Thresholding,andRegion Detection:
Fundamentals, Thresholding, Segmentation by Region Growing
andbyRegionSplittingandMerging,Region SegmentationUsingClustering and
Superpixels, Region Segmentation Using Graph Cuts, Segmentation Using
Morphological Watersheds, Use of Motion in Segmentation


12
V Image Segmentation II: Active Contours: Snakes and Level Sets:
Background, Image Segmentation Using Snakes, Segmentation Using Level
Sets.
Feature Extraction: Background, Boundary Preprocessing, Boundary
FeatureDescriptors,RegionFeatureDescriptors,PrincipalComponents as
FeatureDescriptors,Whole -ImageFeatures,Scale -InvariantFeature
Transform (SIFT)

12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. DigitalImage Processing Gonzalezand
Woods Pearson/Prentice
Hall Fourth 2018
2. FundamentalsofDigital
Image Processing AK.Jain PHI
3. TheImage Processing
Handbook J.C.Russ CRC Fifth 2010

M.Sc(Information Technology) Semester – II
CourseName:ImageProcessing Practical CourseCode: PSIT2P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 90

30  Understandtherelevantaspectsofdigitalimagerepresentationand
their practical implications.
 Havetheabilitytodesignpointwiseintensitytransformations tomeet
stated specifications.
 Understand2 -Dconvolution,the2 -DDFT,andhavetheabitiltyto
design systems using these concepts.
 Haveacommandofbasicimagerestoration techniques.
 Understandtheroleofalternativecolorspaces,andthedesign
requirements leading to choices of color space.
 Appreciatetheutilityofwaveletdecompositionsandtheirroleinimage
processing systems.
 Haveanunderstandingoftheunderlyingmechanismsofimage
compression, and the ability to design systems usingstandard
algorithms to meet design specifications. Course Outcome

Page 91

31 Evaluation Scheme
InternalEvaluation(40 Marks)
Theinternalassessmentmarksshallbeawardedas follows:
1. 30marks(Anyoneofthe following):
a. WrittenTest or
b. SWAYAM(AdvancedCourse)ofminimum20hoursandcertificationexam
completed or
c. NPTEL(AdvancedCourse)ofminimum20hoursandcertificationexam
completed or
d. ValidInternationalCertifications(Prometric,Pearson,Certiport,Coursera,
Udemy and the like)
e. Onecertificationmarksshallbeawardedonecourseonly.Forfourcourses, the
students wil l have to complete four certifications.
2. 10 marks
Themarksgivenoutof40forpublishingtheresearchpapershouldbedividedinto four
course and should awarded out of 10 in each of the four course.

i. SuggestedformatofQuestionpaperof30marksforthewritten test.
Q1. Attempt anytwo ofthe following: 16
a.
b.
c.
d.

Q2. Attempt anytwo ofthe following: 14
a.
b.
c.
d.

ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on
publishing of research paper in UGC approved Journal with plagiarism less than
10%.Themarkscanbeawardedaspertheimpactfactorofthejournal,qualityof the
paper, importance of the contents published, social value.

Page 92

32 ExternalExamination:(60
marks)


Allquestionsare compulsory
Q1 (BasedonUnit1)Attempt anytwo ofthe following: 12
a.
b.
c.
d.

Q2 (BasedonUnit2)Attempt anytwo ofthe following: 12
Q3 (BasedonUnit3)Attempt anytwo ofthe following: 12
Q4 (BasedonUnit4)Attempt anytwo ofthe following: 12
Q5 (BasedonUnit5)Attempt anytwo ofthe following: 12
PracticalEvaluation(50 marks)
ACertifiedcopyjournalisessentialtoappearforthepractical examination.

1. PracticalQuestion 1 20
2. PracticalQuestion 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5

Page 93

33
Security -Major

Semester III Compulsory Course

Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT301 Technical Writing and
Entrepreneurship
Development 60 4 PSIT3P1 60 2 6

Semester IV Compulsory Course
Paper code Paper
Lectures Credit Practical
Hrs Credit Total Credit
Nomenclature Paper
PSIT401 Blockchain 60 4 PSIT4P1 60 2 6

Semester III MSc IT with specialization in Image Processing [MSc IT(AI)]
Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT302d Security Breaches and
Countermeasures 60 4 PSIT3P2d 60 2 6
PSIT303d Malware Analysis 60 4 PSIT3P3d 60 2 6
PSIT304d Offensive Security 60 4 PSIT3P4d 60 2 6
Semester IV MSc IT with specialization in Image Processing [MSc IT(AI)]
Paper
code Paper Lectures Credit Practical Hrs Credit Total
Credit Nomenclature Paper
PSIT402d Cyber Forensics 60 4 PSIT4P2d 60 2 6
PSIT403d Security Operations
Center 60 4 PSIT4P3d 60 2 6
PSIT404d Information Security
Auditing 60 4 PSIT4P4d 60 2 6
PSIT4P4 - Project Implementation and Viva

Page 94

34


















SEMESTER III

Page 95

35


PSIT301: Technical Writing and
Entrepreneurship Development
M. Sc (Information Technology) Semester – III
Course Name: Technical Writing and Entrepreneurship
Development Course Code: PSIT301
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 This course aims to provide conceptual understanding of developing strong foundation in general writing,
including research proposal and reports.
 It covers the technological developing skills for writing Article, Blog, E -Book, Commercial web P age
design, Business Listing Press Release, E -Listing and Product Description.
 This course aims to provide conceptual understanding of innovation and entrepreneurship development.

Unit Details Lectures Outcome
I Introduction to Technical Communication:
What Is Technical Communication? The Challenges of
Producing Technical Communication, Characteristics of a
Technical Document , Measures of Excellence in Technical
Documents, Skills and Qualities Shared by Successful
Workplace Communicators, How Communicati on Skills and
Qualities Affect Your Career? Understanding Ethical and
Legal Considerations: A Brief Introduction to Ethics, Your
Ethical Obligations, Your Legal Obligations, The Role of
Corporate Culture in Ethical and Legal
Conduct,Understanding Ethical an d Legal Issues Related to
Social Media, Communicating Ethically Across Cultures,
Principles for Ethical Communication Writing Technical
Documents:
Planning, Drafting, Revising, Editing, Proofreading Writing
Collaboratively: Advantages and Disadvantages of
Collaboration, Managing Projects, Conducting Meetings,
Using Social Media and Other Electronic Tools in
Collaboration, Importance of Word Press Website, Gender and
Collaboration, Culture and Collaboration . 12 CO1
II Introduction to Content Writing: Types of Content (Article,
Blog, E -Books, Press Release, Newsletters Etc), Exploring
Content Publication Channels. Distribution of your content
across various channels. Blog Creation: Understand the
psychology behind your web traffic, Creating killing landing
pages w hich attract users, Using Landing Page Creators,
Setting up Accelerated Mobile Pages, Identifying UI UX
Experience of your website or blog. Organizing Your
Information: Understanding Three Principles for 12 CO2

Page 96

36 Organizing Technical Information, Understanding
Conven tional Organizational Patterns, Emphasizing
Important Information: Writing Clear, Informative Titles,
Writing Clear, Informative Headings, Writing Clear
Informative Lists, Writing Clear Informative Paragraphs .
III Creating Graphics: The Functions of Graphics, The
Characteristics of an Effective Graphic, Understanding the
Process of Creating Graphics, Using Color Effectively,
Choosing the Appropriate Kind of Graphic, Creating Effective
Graphics for Multicultural Readers. Researching Your
Subject: Unders tanding the Differences Between Academic
and WorkplaceResearch, Understanding the Research Process,
Conducting Secondary Research, Conducting Primary
Research, Research and Documentation: Literature
Reviews, Interviewing for Information, Documenting Source s,
Copyright, Paraphrasing, Questionnaires. Report
Components: Abstracts, Introductions, Tables of Contents,
Executive Summaries, Feasibility Reports, Investigative
Reports, Laboratory Reports, Test Reports, Trip Reports,
Trouble Reports 12 CO3
IV Writing P roposals: Understanding the Process of Writing
Proposals, The Logistics of Proposals, The ―Deliverables‖ of
Proposals, Persuasion and Proposals, Writing a Proposal, The
Structure of the Proposal . Writing Informational Reports:
Understanding the Process of Writing Informational Reports,
Writing Directives, Writing Field Reports, Writing Progress
and Status Reports, Writing Incident Reports, Writing Meeting
Minutes . Writing Recommendation Reports: Understanding
the Role of Recommendation Reports, Using a Probl em-
Solving Model for Preparing Recommendation Reports,
Writing Recommendation Reports . Reviewing, Evaluating,
and Testing Documents and Websites: Understanding
Reviewing, Evaluating, and Testing, Reviewing Documents
and Websites, Conducting Usability Evalu ations, Conducting
Usability Tests, Using Internet tools to check writing Quality,
Duplicate Content Detector, What is Plagiarism?, How to
avoid writing plagiarism content? Innovation management:
an introduction: The importance of innovation, Models of
innov ation, Innovation as a management process . Market
adoption and technology diffusion: Time lag between
innovation and useable product, Innovation and the market
,Innovation and market vision ,Analysing internet search data
to help adoption andforecasting sal es ,Innovative new
products and consumption patterns, Crowdsourcing for new
product ideas, Frugal innovation and ideas from
everywhere,Innovation diffusion theories . 12 CO4
V Managing innovation within firms: Organisations and
innovation, The dilemma of innovation management,
Innovation dilemma in low technology sectors, Dynamic
capabilities, Managing uncertainty, Managing innovation
projects Operations and process innovation: Operations
management, The nature of design and innovation in the
context of operations, Process design, Process design and
innovation
Managing intellectual property: Intellectual property, Trade 12 CO5

Page 97

37 secrets, An introduction to patents, Trademarks, Brand names,
Copyright Management of research and development: What
is research a nd development?, R&D management and the
industrial context, R&D investment and company success,
Classifying R&D, R&D management and its link with
business strategy, Strategic pressures on R&D, Which
business to support and how?, Allocation of funds to R&D,
Level of R&D expenditure Managing R&D
projects: Successful technology management, The changing
nature of R&D management, The acquisition of external
technology, Effective R&D management, The link with the
product innovation process, Evaluating R&D projects .

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Technical Communication
Mike Markel Bedford/St.
Martin's 11 2014
2. Innovation Management and
New Product Development Paul Trott Pearson 06 2017
3. Handbook of Technical
Writing
Gerald J.
Alred , Charles T.
Brusaw , Walter E.
Oliu Bedford/St.
Martin's 09 2008
4. Technical Writing 101: A
Real-World Guide to
Planning and Writing
Technical Content Alan S. Pringle and
Sarah S. O'Keefe scriptorium 03 2009
5. Innovation and
Entrepreneurship Peter Drucker Harper
Business 03 2009



Course Outcomes:
After completion of the course, a student should be able to:
CO1: Develop technical documents that meet the requirements with standard guidelines. Understanding the
essentials and hands -on learning about effective Website Development.
CO2: Write Better Quality Content Which Ranks faster at Search Engines. Build effective Social Media Pages.
CO3: Evaluate the essentials parameters of effective Social Media Pages.
CO4: Understand impo rtance of innovation and entrepreneurship.
CO5: Analyze research and development projects.

Page 98

38




PSIT3P1: Project Documentation and
Viva
M. Sc (Information Technology) Semester – III
Course Name: Project Documentation and Viva Course Code: PSIT3P1
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- --

The learners are expected to develop a project beyond the undergraduate level. Normal web sites, web applications,
mobile apps are not expected. Preferably, the project should be from the elective chosen by the learner at the post
graduate level. In semester three. The learner is supposed to prepare the synopsis and documentation. The same
project has to be implemented in Semester IV.
More details about the project is given is Appendix 1.



























PSIT302d : Security Breaches
and Countermeasures

Page 99

39 M. Sc (Information Technology) Semester – III
Course Name: Security Breaches and Countermeasures Course Code: PSIT302d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 To get the insight of the security loopholes in every aspect of computing.
 To understand the threats and different types of attacks that can be launched on computing systems.
 To know the countermeasures that can be taken to prevent attacks on computing systems.
 To test the software against the attacks.


Unit Details Lectures Outcome
I Introduction to Security Breaching : Overview of
Information Security, Threats and Attack vectors, Concepts
of Hacking – Ethical and Unethical, Information Security
Controls, Concepts of penetration Testing, Information
Security Laws and Standards.
Evaluation Security of IT Organisation: Concepts,
Methodology, Tools, Countermeasures, Penetration
Testing.
Network Scanning: Concepts, Scanning beyond IDS and
firewalls, Tools, Banner Grabbing, Scanning Techniques,
Network Diagrams, penetration testing.
Enumeration: Concepts, Different types of enumeration:
Netbios, SNMP, LDAP, NTP, SMTP, DNS, other
enumeration techniques, Countermeasures, Penetration
Testing 12 CO1
II Analysis of Vulnerability: Concepts, Assessment
Solutions, Scoring Systems, Assessment Tools,
Assessment Reports.
Breaching System Security: Concepts, Cracking
passwords, Escalating privileges, Executing Applications,
Hiding files, covering tracks, penetration testing.
Threats due to malware: Concepts, Malware Analysis,
Trojan concepts, countermeasures, Virus and worm
concepts, anti -malware software, penetration testing.
Network Sniffing: Concepts, countermeasures, sniffing
techniques, detection techniques, tools, penetration testing. 12 CO2
III Social Engineering: Concepts, Impersonation on
networking sites, Techniques, Identity theft, Insider threats,
countermeasures, Pen testing.
Denial of Service and Distributed Denial of service:
Concepts, techniques, botnets, attack tools,
countermeasures, protection tools, pen etration testing.
Hijacking an active session: Concepts, tools, application
level session hijacking, countermeasures, network level
session hijacking, penetration testing.
Evasion of IDS, Firewalls and Honeypots: Introduction
and concepts, detecting honeyp ots, evading IDS, IDS and
Firewall evasion countermeasures, evading firewalls, 12 CO3

Page 100

40 penetration testing.
IV Compromising Web Servers: Concepts,attacks, attack
methodology, attack tools, countermeasures, patch
management, web server security tools, penetration testing.
Compromising Web Applications: Concepts , threats,
methods, tools, countermeasures, testing tools, penetration
testing.
Performing SQL Injection: Concepts,
types,methodology,tools, techniques, countermeasures.
Compromising Wireless Netw orks: Concepts,wireless
encryption, threats, methodology, tools, compromising
Bluetooth, countermeasures, wireless security tools,
penetration testing. 12 CO4
V Compromising Mobile Platforms: Attack vectors,
Compromising Android OS, Compromising iOS, Mobi le
spyware, Mobile Device Management, Mobile security,
penetration testing.
Compromising IoT: Concepts,attacks, compromising
methodology, tools, countermeasures, penetration testing.
Cloud Security: Concepts, Security, threats, attacks, tools,
penetration testing.
Cryptography: Concepts, email encryption, algorithms,
disk encryption, tools, cryptanalysis, Public key
infrastructure, countermeasures. 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. CEHv10, Certified Ethical
Hacker Study Guide Ric Messier Sybex - Wiley - 2019
2. All in One, Certified Ethical
Hacker Matt Walker Tata McGraw
Hill - 2012
3. CEH V10: EC -Council
Certified Ethical Hacker
Complete Training Guide I.P. Specialist IPSPECIALIST - 2018

M. Sc (Information Technology) Semester – III
Course Name: Security Breaches and Countermeasures
Practical Course Code: PSIT3P3d
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcome:
CO1: The student should be able to identify the different security breache s that can occur. The student should be
able to evaluate the security of an organization and identify the loopholes. The student should be able to perform
enumeration and network scanning.

CO2: The student should be able to identify the vulnerability in t he systems, breach the security of the system,

Page 101

41 identify the threats due to malware and sniff the network. The student should be able to do the penetration testing to
check the vulnerability of the system towards malware and network sniffing.

CO3: The student should be able to perform social engineering and educate people to be careful from attacks due
to social engineering, understand and launch DoS and DDoS attacks, hijack and active session and evade IDS and
Firewalls. This should help the studen ts to make the organization understand the threats in their systems and build
robust systems.

CO4: The student should be able to identify the vulnerabilities in the Web Servers, Web Applications, perform
SQL injection and get into the wireless networks. T he student should be able to help the organization aware about
these vulnerabilities in their systems.

CO5: The student should be able to identify the vulnerabilities in the newer technologies like mobiles, IoT and
cloud computing. The student should be a ble to use different methods of cryptography.


PSIT303d : Malware Analysis
M. Sc (Information Technology) Semester – III
Course Name: Malware Analysis Course Code: PSIT303d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 Possess the skills necessary to carry out independent analysis of modern malware samples using both static
and dynamic analysis techniques.
 Have an intimate understanding of executable formats, Windows internals and API, and analysis
techniques.
 Extract investigative leads from host and network -based indicators associated with a malicious program.
 Apply techniques and concepts to unpack, extract, decrypt, or bypass new anti­analysis tech niques in future
malware samples.
 Achieve proficiency with industry standard tools including IDA Pro, OllyDbg, WinDBG, PE Explorer,
ProcMon etc.

Unit Details Lectures Outcome
I Malware Analysis: Introduction, Techniques, Types of
malware, General rules for Malware Analysis. Basic Static
Techniques: Antivirus Scanning,Hashing, Finding Strings,
Packed and Obfuscated Malware, Portable Executable
Malware, Portable executable File Format, Linked Librar ies
and Functions, Static Analysis, The PE file headers and
sections. Malware Analysis in Virtual Machines: Structure
of VM, Creating and using Malware Analysis machine, Risks
of using VMware for malware analysis, Record/Replay. Basic
Dynamic Analysis: Sandboxes, Running Malware,
Monitoring with process monitor, Viewing processes with
process explorer, Comparing registry snapshots with regshot,
Faking a network, Packet sniffing with Wireshark, Using
INetSim, Basic Dynamic Tools. x86 Disassembly 12 CO1
II IDA PRO: Loading an executable, IDA Pro Interface, Using
cross references, Analysing functions, Using graphing options, 12 CO2

Page 102

42 Enhancing disassembly, Extending IDA with plug -ins.
Recognising C Code constructs in assembly: Global v/s
local variables, Disassembling arithmetic operations,
recognizing if statements, recognizing loops, function call
conventions, Analysing switch statements, Disassembling
arrays, Identifying structs, Analysing linked list traversal.
Analysing Malicious Windows Programs: The windows
API, The Windows Registry, Networking APIs,
Understanding running malware. Kernel v/s user mode,
Native API.
Advanced Dynamic Analysis – Debugging: Source -level
v/sAssembly -level debugging, kernel v/s user mode
debugging, Using a debugger, Exceptions, Modifyi ng
execution with a debugger, modifying program execution.
III Advanced Dynamic Analysis – OLLYDBG: Loading
Malware, The Ollydbg Interface, Memory Map, Viewing
threads and Stacks, Executing code, Breakpoints, Loading
DLLs, Tracing, Exception handli ng, Patching, Analysing shell
code, Assistance features, Plug -ins, Scriptable debugging.
Kernel Debugging with WINDBG: Drivers and kernel code,
Using WinDbg, Microsoft Symbols, kernel debugging and
using it, Rootkits, Loading drivers, kernel issues with
windows.
Malware Functionality – Malware Behavior: Downloaders
and launchers, Backdoors, Credential stealers, Persistence
mechanisms, Privilege escalation, covering the tracks.
Covert Malware Launching: Launchers, Process injection,
Process replacement, Hoo k injection, detours, APC injection. 12 CO3
IV Data Encoding: Goal of Analysing algorithms, Simple
ciphers, Common cryptographic algorithms, Custom
encoding, decoding.
Malware – focused network signatures: Network
countermeasures ,Safely investigating attacker online, Content -
Based Network Countermeasures, Combining Dynamic and
Static Analysis Techniques, Understanding the Attacker’s
Perspective .
Anti -disassembly: Concepts, Defeating disassembly
algorithms, anti -disassembly techniques, Ob scuring flow
control, Thwarting stack -frame analysis.
Anti -debugging: Windows debugger detection, debugger
behavior, Interfering with debugger functionality, Debugger
vulnerabilities. 12 CO4
V Anti -virtual machine techniques: VMWare artifacts,
Vulnerable functions, Tweaking settings, Escaping the virtual
machine.
Packers and unpacking: Packer anatomy, Identifying Packed
Programs, Unpacking options, Automated Unpacking, Manual
Unpacking, Common packers, Analysing without unpacking,
Packed DLLs,
Shellcode Analysis: Loading shellcode for analysis, Position -
independent Code, Identifying Execution Location, Manual
Symbol Resolution, Shellcode encoding, NOP Sleds, Finding
Shellcode.
C++ Analysis: OOP,Virtual and Non -virtual functions,
Creating and destroying ob jects. 12 CO5

Page 103

43 64-bit Malware: Why 64 -bit malware? Differences in x64
architecture, Windows 32 -bit on Windows 64 -bit, 64 -bit hints
at malware functionality.

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Practical Malware Analysis
– The Hands -On Guide to
Dissecting Malicious
Software Michael Sikorski,
Andrew Honig No Scratch
Press - 2013
2. Mastering Malware Analysis
Alexey Kleymenov,
Amr Thabet Packt
Publishing - 2019
3. Windows Malware Analysis
Essentials Victor Marak Packt
Publishing 2015

M. Sc (Information Technology) Semester – III
Course Name: Malware Analysis Practical Course Code: PSIT3P3d
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:
After completion of the course, a student should be able to:
CO1: Understand various introductory techniques of malware analysis and creating the testing environment
CO2: Perform advanced dynamic analysis and recognize constructs in assembly code.
CO3: Perform Reverse Engineering using OLLYDBG and WINDBG and study the behaviours and functions of
malware
CO4: Understand data encoding, various techniques for anti -disassembly and anti -debugging
CO5: Understand various anti virtual machine techniques and perform shellcode analysis of various languages
along with x64 archite cture.

Page 104

44 PSIT304d :Offensive Security
M. Sc (Information Technology) Semester – III
Course Name: Offensive Security Course Code: PSIT304d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 Understanding of security requirements within an organization
 How to inspect, protect assets from technical and managerial perspectives
 To Learn various offensive strategies to penetrate the organizations security.
 To learn various tools that aid in offensive security testing.

Unit Details Lectures Outcome
I Fault Tolerance and Resilience in Cloud Computing
Environments, Securing Web Applications, Services, and
Servers, Wireless Network Security, Wireless Sensor
Network Security: The Internet of Things, Security for the
Internet of Things, Cellular Network Security 12 CO1
II Social Engineering Deceptions and Defenses, What Is
Vulnerability Assessment, Risk Management, Insider Threat,
Disaster Recovery, Security P olicies and Plans Development 12 CO2
III Introduction to Metasploit and Supporting Tools
The importance of penetration testing
Vulnerability assessment versus penetration testing
The need for a penetration testing framework
Introduction to Metasploit
When to use Metasploit?
Making Metasploit effective and powerful using
supplementary tools
Nessus NMAP w3af Armitage
Setting up Your Environment
Using the Kali Linux virtual machine - the easiest way
Installing Metasploit on Windows Installing Metasplo it on
Linux Setting up exploitable targets in a virtual environment
Metasploit Components and Environment Configuration
Anatomy and structure of Metasploit
Metasploit components
Auxiliaries Exploits Encoders Payloads
Post, Playing around with msfconsole
Variables in Metasploit
Updating the Metasploit Framework 55 12 CO3
IV Information Gathering with Metasploit
Information gathering and enumeration
Transmission Control Protocol User Datagram Protocol
File Transfer Protocol
Server Message Block Hypertext Transfer Protocol Simple
Mail Transfer Protocol
Secure Shell Domain Name System
Remote Desktop Protocol 12 CO4

Page 105

45 Password sniffing
Advanced search with shodan
Vulnerability Hunting with Metasploit Managing the
database
Work spaces Importing scans
Backing up the database NMAP
NMAP scanning approach Nessus
Scanning using Nessus from msfconsole
Vulnerability detection with Metasploit auxiliaries
Auto exploitation with db_autopwn
Post exploitation What is meterpreter?
Searching for content Screen capture
Keystroke logging Dumping the hashes and cracking with
JTR Shell command
Privilege escalation
Client -side Attacks with Metasploit
Need of client -side attacks
What are client -side attacks?
What is a Shellcode? Wha t is a reverse shell? What is a bind
shell? What is an encoder? The msfvenom utility
Generating a payload with msfvenom
Social Engineering with Metasploit
Generating malicious PDF
Creating infectious media drives
V Approaching a Penetration Test Using Metasploit
Organizing a penetration test
Preinteractions
Intelligence gathering/reconnaissance phase Predicting the
test grounds
Modeling threats Vulnerability analysis
Exploitation and post -exploitation
Reportin g Mounting the environment
Setting up Kali Linux in virtual environment
The fundamentals of Metasploit
Conducting a penetration test with Metasploit Recalling the
basics of Metasploit
Benefits of penetration testing using Metasploit Open source
Supp ort for testing large networks and easy naming
conventions
Smart payload generation and switching mechanism Cleaner
exits The GUI environment
Penetration testing an unknown network Assumptions
Gathering intelligence Using databases in Metasploit
Modeling threats
Vulnerability analysis of VSFTPD backdoor The attack
procedure
The procedure of exploiting the vulnerability
Exploitation and post exploitation
Vulnerability analysis of PHP -CGI query string parameter
vulnerability
Exploitation and post exploitation
Vulnerability analysis of HFS
Exploitation and post exploitation
Maintaining access
Clearing tracks 12 CO5

Page 106

46 Revising the approach
Reinventing Metasploit Ruby – the heart of Metasploit
Creating your first Ruby program
Interacting with the Ruby shell
Defining methods in the shell
Variables and data types in Ruby
Working with strings Concatenating strings The substring
function The split function Numbers and conversions in
Ruby Conversions in Ruby Ranges in Ruby Arrays in Ruby
Methods in Ruby
Decision -making operators Loops in Ruby
Regular expressions Wrapping up with Ruby basics
Developing custom modules Building a module in a
nutshell
The architecture of the Metasploit framework Understanding
the file structure Th e libraries layout Understanding the
existing modules
The format of a Metasploit module
Disassembling existing HTTP server scanner module
Libraries and the function
Writing out a custom FTP scanner module
Libraries and the function Using msftidy
Writin g out a custom SSH authentication brute forcer
Rephrasing the equation
Writing a drive disabler post exploitation module Writing a
credential harvester post exploitation module Breakthrough
meterpreter scripting
Essentials of meterpreter scripting
Pivoting the target network Setting up persistent access API
calls and mixins
Fabricating custom meterpreter scripts
Working with RailGun
Interactive Ruby shell basics
Understanding RailGun and its scripting
Manipulating Windows API calls
Fabricating sophisticated RailGun scripts
The Exploit Formulation Process
The absolute basics of exploitation
The basics The architecture System organization basics
Registers
Exploiting stack -based buffer overflows with Metasploit
Crashing the vulnera ble application
Building the exploit base Calculating the offset Using the
pattern_create tool
Using the pattern_offset tool Finding the JMP ESP address
Using Immunity Debugger to find executable modules
Using msfbinscan Stuffing the space
Relevance of NOPs Determining bad characters Determining
space limitations
Writing the Metasploit exploit module
Exploiting SEH -based buffer overflows with Metasploit
Building the exploit base Calculating the offset Using
pattern_create tool Using pattern_offset to ol Table of
Contents
Finding the POP/POP/RET address

Page 107

47 The Mona script Using msfbinscan
Writing the Metasploit SEH exploit module Using NASM
shell for writing assembly instructions Bypassing DEP in
Metasploit modules Using msfrop to find ROP gadgets
Using Mona to create ROP chains Writing the Metasploit
exploit module for DEP bypass

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Computer and Information
Security Handbook John R. Vacca Morgan
Kaufmann
Publisher 3rd 2017
2. Metasploit Revealed: Secrets of
the Expert Pentester Sagar Rahalkar Packt
Publishing 2017

M. Sc (Information Technology) Semester – III
Course Name: Offensive Security Practical Course Code: PSIT3P4d
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.



Course Outcomes:

After completion of the course, a student should be able to:
CO1: Understand basic security issues in cloud, IoT etc.
CO2: Understand different security techniques and policies
CO3: Use Vulnerability assessment and exploitation tool
CO4: Analyze the network perform reconnaissance and enumerate the target to detect vulnerabilities
CO5: Perform offensive tests using Metasploit on various application, generating payloads etc.

Page 108

48




SEMESTER IV

Page 109

49

PSIT401: Blockchain – Compulsory
Course
M. Sc ( Information Technology ) Semester – IV
Course Name: Blockchain Course Code: PSIT401
Periods per week (1 Period is 6 0 minutes ) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 To provide conceptual understanding of the function of Blockchain as a method of securing distributed
ledgers, how consensus on their contents is achieved, and the new applications that they enable.
 To cover the technological underpinnings of blockchain operations a s distributed data structures and
decision -making systems, their functionality and different architecture types.
 To provide a critical evaluation of existing ―smart contract‖ capabilities and platforms, and examine their
future directions, opportunities, risks and challenges.

Unit Details Lectures Outcome
I Blockchain: Introduction, History, Centralised versus
Decentralised systems, Layers of blockchain, Importance of
blockchain, Blockchain uses and use cases.
Working of Blockchain: Blockchain foundation,
Cryptography, Game Theory, Computer Science
Engineering, Properties of blockchain solutions, blockchain
transactions, distributed consensus mechanisms, Blockchain
mechanisms, Scaling blockchain
Working of Bitcoin: Money, Bitcoin, B itcoin blockchain,
bitcoin network, bitcoin scripts, Full Nodes and SVPs,
Bitcoin wallets. 12 CO1
II Ethereum: three parts of blockchain, Ether as currency and
commodity, Building trustless systems, Smart contracts,
Ethereum Virtual Machine, The Mist browser, Wallets as a
Computing Metaphor, The Bank Teller Metaphor, Breaking
with Banking History, How Encryption L eads to Trust,
System Requirements, Using Parity with Geth, Anonymity
in Cryptocurrency, Central Bank Network, Virtual
Machines, EVM Applications, State Machines, Guts of the
EVM, Blocks, Mining’s Place in the State Transition
Function, Renting Time on the EVM, Gas, Working with
Gas, Accounts, Transactions, and Messages, Transactions
and Messages, Estimating Gas Fees for Operations, Opcodes
in the EVM.
Solidity Programming: Introduction,Global Banking Made
Real, Complementary Currency, Programming the EVM,
Design Rationale, Importance of Formal Proofs, Automated
Proofs, Testing, Formatting Solidity Files, Reading Code,
Statements and Expressions in Solidity, Value Types, Global
Special Variables, Units, and Functions, 12 CO2

Page 110

50 III Hyperledger: Overview, Fabric, composer, installing
hyperledger fabric and composer, deploying, running the
network, error troubleshooting.
Smart Contracts and Tokens: EVM as Back End, Assets
Backed by Anything, Cryptocurrency Is a Measure of Time,
Function of Collecti bles in Human Systems, Platforms for
High -Value Digital Collectibles, Tokens as Category of
Smart Contract, Creating a Token, Deploying the Contract,
Playing with Contracts. 12 CO3
IV Mining Ether: Why? Ether’s Source, Defining Mining,
Difficulty, Self -Regulation, and the Race for Profit, How
Proof of Work Helps Regulate Block Time, DAG and
Nonce, Faster Blocks, Stale Blocks, Difficulties, Ancestry of
Blocks and Transactions, Ethereum and Bitcoin, Fo rking,
Mining, Geth on Windows, Executing Commands in the
EVM via the Geth Console, Launching Geth with Flags,
Mining on the Testnet, GPU Mining Rigs, Mining on a Pool
with Multiple GPUs.
Cryptoecnomics: Introduction, Usefulness of
cryptoeconomics, Speed o f blocks, Ether Issuance scheme,
Common Attack Scenarios. 12 CO4
V Blockchain Application Development: Decentralized
Applications, Blockchain Application Development,
Interacting with the Bitcoin Blockchain, Interacting
Programmatically with Ethereum —Sending Transactions,
Creating a Smart Contract, Executing Smart Contract
Functions, Public vs. Private Blockchains, Decentralized
Application Architecture, Building an Ethereum
DApp: The DApp, Setting Up a Private Ethereum Network,
Creating the Smart Contract , Deploying the Smart Contract,
Client Application, DApp deployment: Seven Ways to
Think About Smart Contracts, Dapp Contract Data Models,
EVM back -end and front -end communication, JSON -RPC,
Web 3, JavaScript API, Using Meteor with the EVM,
Executing Contr acts in the Console, Recommendations for
Prototyping, Third -Party Deployment Libraries, Creating
Private Chains. 12 CO5





Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Beginning Blockchain
A Beginner’s Guide to
Building Blockchain
Solutions Bikramaditya Singhal,
Gautam Dhameja,
Priyansu Sekhar Panda Apress 2018
2. Introducing Ethereum and
Solidity Chris Dannen Apress 2017
3. The Blockchain Developer EladElrom Apress 2019
4. Mastering Ethereum Andreas M.
Antonopoulos
Dr. Gavin Wood O’Reilly First 2018
5. Blockchain Enabled Vikram Dhillon Apress 2017

Page 111

51 Applications David Metcalf
Max Hooper

M. Sc ( Information Technology ) Semester – III
Course Name: Blockchain Course Code: PSIT
Periods per week (1 Period is 6 0 minutes ) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.


Course Outcome s:
After completion of the course, a student should be able to:

CO1: The students would understand the structure of a blockchain and why/when it is better than a simple
distributed database.

CO2: Analyze the incentive structure in a blockchain based system and critically assess its functions, benefits and
vulnerabilities

CO3: Evaluate the setting where a blockchain based structure may be applied, its potential and its limitations

CO4: Understand what constitutes a ―smart‖ contract, wh at are its legal implications and what it can and cannot do,
now and in the near future

CO5: Develop blockchain DApps.

Page 112

52
PSIT402d: Cyber Forensics
M. Sc (Information Technology) Semester – IV
Course Name: Cyber Forensics Course Code: PSIT402d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 Explain laws relevant to computer forensics
 Seize digital evidence from pc systems
 Recover data to be used as evidence
 Analyse data and reconstruct events
 Explain how data may be concealed or hidden

Unit Details Lectures Outcome
I Computer Forensics: The present Scenario, The
Investigation Process, Computers – Searching and Seizing,
Electronic Evidence, Procedures to be followed by the first
responder. 12 CO1
II Setting up a lab for Computer Forensics, Hard Disks and File
Systems, Forensics on Windows Machine, Acquire and
Duplicate Data 12 CO2
III Recovery of deleted files and partitions, Using Access Data
FTK and Encase for forensics Investigation, Forensic
analysis of Steganography and Image files, Cracking
Application passwords. 12 CO3
IV Capturing logs and correlating to the events, Network
Forensics – Investigating logs and Network traffic,
Investigating Wireless and Web Attacks. 12 CO4
V Email Tracking and Email Crime investigation. Mobile
Forensics, Reports of Investigation, Become an expert
witness. 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. EC-Council CHFIv10 Study
Guide -- EC-Council -- 2018
2. The official CHFI Exam
312-49 study Guide Dave Kleiman SYNGRESS -- 2007
3. Digital Forensics and
Incident Response Gerard Johansen Packt
Publishing -- 2020
4. Practical Cyber Forensics Niranjan Reddy Apress -- 2019


M. Sc (Information Technology) Semester – IV
Course Name: Cyber Forensics Practical Course Code: PSIT4P2d
Periods per week (1 Period is 60 minutes) 4
Credits 2

Page 113

53 Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -


List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.


Course Outcomes:
After completion of the course, a student should be able to:
CO1: Investigate the cyber forensics with standard operating procedures.
CO2: Recover the data from the hard disk with legal procedure.
CO3: To recover and analyse the data using forensics tool
CO4: Acquire the knowledge of network analysis and use it for anal ysing the internet attacks.
CO5: Able to investigate internet frauds done through various gadgets like mobile, laptops, tablets and
become a forensic investigator.


PSIT403d : Security Operations
Centre
M. Sc (Information Technology) Semester – IV
Course Name: Security Operations Centre Course Code: PSIT403d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 The SOC (Security Operations Centre) allows an organization to enforce and test its security policies,
processes, procedures and activities through one central platform that monitors and evaluates the
effectiveness of the individual elements and the overall security system of the organization .
 This will also allow the learners to configure various use cases and detect various attacks across the
network and report them in real time and also take appropriate actions.
 This course will cover the design, deployment and operation of the SOC.
 Once t his course is completed, students will have the skills to perform your SOC responsibilities
effectively.

Unit Details Lectures Outcome

Page 114

54 I Introduction to Security Operations Management
Foundation Topics Introduction to Identity and Access
Management Phases of the Identity and Access Lifecycle
Registration and Identity Validation Privileges Provisioning
Access Review Access Revocation Password Management
Password Creation Password Storage and Transmission
Password Reset Password Synchronization
Directo ry Management Single Sign -On
Kerberos Federated SSO Security Assertion Markup
Language OAuth OpenID Connect
Security Events and Logs Management
Logs Collection, Analysis, and Disposal
Syslog Security Information and Event Manager Assets
Management Assets I nventory Assets Ownership Assets
Acceptable Use and Return Policies Assets Classification
Assets Labeling Assets and Information Handling Media
Management
Introduction to Enterprise Mobility Management Mobile
Device Management
Configuration and Change Mana gement
Configuration Management Change Management
Vulnerability Management
Vulnerability Identification Finding Information about a
Vulnerability Vulnerability Scan Penetration Assessment
Product Vulnerability Management
Vulnerability Analysis and Prioritization
Vulnerability Remediation Patch Management References
and Additional Readings
Fundamentals of Cryptography and Public Key
Infrastructure (PKI)
Cryptography Ciphers and Keys
Ciphers Keys Block and Stream Ciphers
Symmetric and Asymmetric Algor ithms
Symmetric Algorithms Asymmetric Algorithms Hashes
Hashed Message Authentication Code Digital Signatures
Digital Signatures in Action Key Management
Next -Generation Encryption Protocols
IPsec and SSL IPsec SSL Fundamentals of PKI Public and
Private Ke y Pairs RSA Algorithm, the Keys, and Digital
Certificates
Certificate Authorities Root and Identity Certificates Root
Certificate Identity Certificate X.500 and X.509v3
Certificates
Authenticating and Enrolling with the CA
Public Key Cryptography Standards
Simple Certificate Enrollment Protocol
Revoking Digital Certificates Using Digital Certificates PKI
Topologies Single Root CA
Hierarchical CA with Subordinate CAs
Cross -certifying CAs Exam Preparation Tasks
Review All Key Topics Complete Tables and Lists from
Memory
Introduction to Virtual Private Networks (VPNs)
What Are VPNs? Site -to-site vs. Remote -Access VPNs An
Overview of IPsec IKEv1 Phase 1 IKEv1 Phase 2 IKEv2 12 CO1

Page 115

55 SSL VPNs
SSL VPN Design Considerations User Connectivity VPN
Device Feature Set
Infrastructure Planning Implementation Scope
II Windows -Based Analysis
Process and Threads Memory Allocation
Windows Registration Windows Management
Instrumentation Handles Services
Windows Event Logs Exam Preparation Tasks
Linux - and Mac OS X–Based Analysis
Processes Forks Permissions Symlinks
Daemons UNIX -Based Syslog
Apache Access Logs
Endpoint Security Technologies
Antimalware and Antivirus Software
Host -Based Firewalls and Host -Based Intrusion Prevention
Application -Level Whitelisting an d Blacklisting System -
Based Sandboxing
12 CO2
III Threat Analysis
What Is the CIA Triad: Confidentiality, Integrity, and
Availability?
Confidentiality Integrity Availability
Threat Modeling Defining and Analyzing the Attack Vector
Understanding the Attack Complexity Privileges and User
Interaction
The Attack Scope Exam Preparation Tasks
Forensics
Introduction to Cybersecurity Forensics
The Role of Attribution in a Cybersecurity Investigation The
Use of Digital Evidence
Defining Digital Forensic Evid ence
Understanding Best, Corroborating, and Indirect or
Circumstantial Evidence
Collecting Evidence from Endpoints and Servers Collecting
Evidence from Mobile Devices Collecting Evidence from
Network Infrastructure Devices Chain of Custody
Fundamentals of Microsoft Windows Forensics Processes,
Threads, and Services
Memory Management Windows Registry
The Windows File System Master Boot Record (MBR) The
Master File Table (MFT)
Data Area and Free Space FAT
NTFS MFT Timestamps, MACE, and Alternate Data
Streams EFI Fundamentals of Linux Forensics Linux
Processes Ext4
Journaling Linux MBR and Swap File System
Exam Preparation Tasks
Fundamentals of Intrusion Analysis
Common Artifact Elements and Sources of Security Events
False Positives, False Negatives, True Po sitives, and True
Negatives
Understanding Regular Expressions
Protocols, Protocol Headers, and Intrusion Analysis Using
Packet Captures for Intrusion Analysis Mapping Security 12 CO3

Page 116

56 Event Types to Source Technologies
IV Introduction to Incident Response and the Incident
Handling Process
Introduction to Incident Response
What Are Events and Incidents? The Incident Response Plan
The Incident Response Process
The Preparation Phase The Detection and Analysis Phase
Containment, Eradication, and Recovery Post -Incident
Activity (Postmortem) Information Sharing and
Coordination Incident Response Team Structure The
Vocabulary for Event Recording and Incident Sharing
(VERIS)
Incident Response Teams
Computer Security Incident Response Teams (CSIRTs)
Product Security Incident Response Teams (PSIRTs)
Security Vulnerabilities and Their Severity Vulnerability
Chaining Role in Fixing Prioritization Fixing Theoretical
Vulnerabilities Internally Versu s Externally Found
Vulnerabilities National CSIRTs and Computer Emergency
Response Teams (CERTs) Coordination Centers Incident
Response Providers and Managed Security Service Providers
(MSSPs)
Compliance Frameworks
Payment Card Industry Data Security Stand ard (PCI DSS)
PCI DSS Data
Health Insurance Portability and Accountability Act
(HIPAA) HIPAA Security Rule HIPAA Safeguards
Administrative Safeguards Physical Safeguards Technical
Safeguards Sarbanes -Oxley (SOX) Section 302 Section 404
Section 409 SOX Audi ting Internal Controls
Network and Host Profiling
Network Profiling Throughput Measuring Throughput Used
Ports Session Duration
Critical Asset Address Space Host Profiling
Listening Ports Logged -in Users/Service Accounts Running
Processes Applications 12 CO4
V The Art of Data and Event Analysis
Normalizing Data Interpreting Common Data Values into a
Universal Format Using the 5 -Tuple Correlation to Respond
to Security Incidents Retrospective Analysis and Identifying
Malicious Files Identifying a Malicious File Mapping Threat
Intelligence with DNS and Other Artifacts
Deterministic Versus Probabilistic Analysis
Intrusion Event Categories
Diamond Model of Intrusion
Cyber Kill Chain Model Reconnaissance
Weaponization Delivery Exploitation
Installation Command and Control Action and Objectives
Types of Attacks and Vulnerabilities
Types of Attacks Reconnaissance Attacks
Social Engineering Privilege Escalation Attacks Backdoors
Code Execution
Man-in-the Middle Attacks Denial -of-Service Attacks Direct
DDoS Botnets Participating in DDoS Attacks Reflected
DDoS Attacks 12 CO5

Page 117

57 Attack Methods for Data Exfiltration ARP Cache Poisoning
Spoofing Attacks Route Manipulation Attacks Password
Attacks
Wireless Attacks Types of Vulnerabilities
Security Evasion Techniques
Key Encryption and Tunneling Concepts
Resource Exhaustion Traffic Fragmentation
Protocol -Level Misinterpretation Traffic Timing,
Substitution, and Insertion Pivoting

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. CCNA Cyber Ops SECOPS
210-255 Official Cert Guide Omar Santos,Joseph
Muniz CISCO 1st 2017
2. CCNA Cyber Ops SECFND
210-250 Official Cert Guide Omar Santos,Joseph
Muniz CISCO 1st 2017
3. CCNA Cyber security
Operations Companion
Guide CISCO 1st 2018

M. Sc (Information Technology) Semester – IV
Course Name: Security Operations Centre Practical Course Code: PSIT4P3d
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.

Course Outcomes:
After completion of the course, a student should be able to:
CO1: Understanding basics of SOC, Cryptography and managing and deploying VPNs.
CO2: Analyse Windows and Linux based logs along with logs generated by endpoints.
CO3: Understand and analyze various forms of intrusions, threats and perform forensic analysis on them.
CO4: Understand the incident response process and handle incidents by adhering to compliance policies and
standards set by the organization.
CO5: Understand the various types of attacks and vulnerabilities, categorize events and perform incident analysis.

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58
PSIT404d : Information Security
Auditing
M. Sc (Information Technology) Semester – IV
Course Name: Information Security Auditing Course Code: PSIT404d
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 Understand various information security policies in place.
 Assess an organization based on the needs and suggest the requisite information security policies to be
deployed.
 Audit the organization across relevant policies and assist the organization in implementing such policies
along with suggesting improvements.

Unit Details Lectures Outcome
I Secrets of a Successful Auditor
Understanding the Demand for IS Audits
Understanding Policies, Standards, Guidelines, and Procedures
Understanding Professional Ethics Understanding the Purpose
of an Audit Differentiating between Auditor and Auditee Roles
Implementing Audit Standards Auditor Is an Executive Position
Understanding the Corporate Organizational Structure
Governance
Strategy Planning for Organizational Control
Overview of Tactical Management Planning and Performance
Overview of Business Process Reengineering Operations
Management Summary
Audit Process
Understanding the Audit Program Establishing and Approving
an Audit Charter
Preplanning Specific Audits Performing an Audit Risk
Assessment Determining Whether an Audit Is Possible
Performing the A udit
Gathering Audit Evidence Conducting Audit Evidence Testing
Generating Audit Findings
Report Findings Conducting Follow -up (Closing Meeting) 12 CO1
II Information Systems Acquisition and Development
Project Governance and Management
Business Case and Feasibility Analysis
System Development Methodologies
Control Identification and Design
Testing Methodologies
Configuration and Release Management
System Migration, Infrastructure Deployment and Data
Conversion
Post-implementation Review 12 CO2

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59 III Information Systems Operations
Introduction
Common Technology Components
IT Asset Management
Job Scheduling and Production Process Automation
System Interfaces
End-user Computing
Data Governance
Systems Performance Management
Problem and Incident Managemen t
Change, Configuration, Release and
IT Service Level Management
Database Management
Business Resilience
Business Impact Analysis
Data Backup, Storage and Restoration
Business Continuity Plan
Disaster Recovery Plans 12 CO3
IV Information Systems Life Cycle
Governance in Software Development
Management of Software Quality
Overview of the Executive Steering Committee Change
Management
Management of the Software Project
Overview of the System Development Life Cycle Overview of
Data Architecture
Decision S upport Systems Program Architecture Centralization
vs. Decentralization Electronic Commerce
System Implementation and Operations
Understanding the Nature of IT Services
Performing IT Operations Management
Performing Capacity Management
Using Administrative Protection
Performing Problem Management
Monitoring the Status of Controls
Implementing Physical Protection 12 CO4
V Protecting Information Assets
Understanding the Threat
Using Technical Protection
Business Continuity and Disaster Recovery
Debunking the Myths Understanding the Five Conflicting
Disciplines Called Business Continuity Defining Disaster
Recovery Defining the Purpose of Business Continuity Uniting
Other Plans with Business Continuity Understanding the Five
Phases of a Business Continuity Prog ram Understanding the
Auditor Interests in BC/DR Plans 12 CO5



Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. CISA®: Certified Information
Systems Auditor David Cannon SYBEX Fourth
Edition 2016
2. CISA Review Manual 27th
Edition ISACA 2019

Page 120

60 3. CISA Certified Information
Systems Auditor All -in-One
Exam Guide, Fourth Edition, O’Reilly 4th
Edition 2019


Course Outcomes:

After completion of the course, a student should be able to:

CO1: Understand various information security policies and process flow, Ethics of an Information security
Auditor.

CO2: Understand various information systems in an organization, their criticality and various governance and
management policies associated with them.

CO3: Critically analyse various operational strategies like asset management, data governance etc. and suggest
requisite changes as per organizations requirements with improvements.

CO4: Understand the information flow across the organization and identify the weak spots, and also suggest
improvements to strengthen them.

CO5: Come up with strong strategies to protect information assets and come up with an efficient business
continuity plan, disaster recovery strategy etc.

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61 PSIT4P4: Project Implementation and
Viva
M. Sc (Information Technology) Semester – IV
Course Name: Project Implementation and Viva Course Code: PSIT4P4
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -

The project dissertation and Viva Voce details are given in Appendix 1.





Evaluation Scheme
Internal Evaluation (40 Marks)
The internal assessment marks shall be awarded as follows:
1. 30 marks (Any one of the following):
a. Written Test or
b. SWAYAM (Advanced Course) of minimum 20 hours and certification exam completed or
c. NPTEL (Advanced Course) of minimum 20 hours and certification exam completed or
d. Valid International Certifications (Prometric, Pearson, Certiport, Coursera, Udemy and the
like)
e. One certification marks shall be awarded one course only. For four courses, the students will
have to complete four certifications.
2. 10 marks
The marks given out of 40 (30 in Semester 4) for publishing the research paper should be divided
into fo ur course and should awarded out of 10 in each of the four course.

i. Suggested format of Question paper of 30 marks for the written test.
Q1. Attempt any two of the following: 16
a.
b.
c.
d.

Q2. Attempt any two of the following: 14
a.
b.
c.
d.

ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on publishing of
research paper in UGC approved / Other Journal with plagiarism less than 10%. The marks can be
awarded as per the impact factor of the journal, quality of the pap er, importance of the contents
published, social value.

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62 External Examination: (60 marks)

All questions are compulsory
Q1 (Based on Unit 1) Attempt any two of the following: 12
a.
b.
c.
d.

Q2 (Based on Unit 2) Attempt any two of the following: 12
Q3 (Based on Unit 3) Attempt any two of the following: 12
Q4 (Based on Unit 4) Attempt any two of the following: 12
Q5 (Based on Unit 5) Attempt any two of the following: 12

Practical Evaluation (50 marks)

A Certified copy of hard -bound journal is essential to appear for the practical examination.

1. Practical Question 1 20
2. Practical Question 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5


Project Documentation and Viva Voce
Evaluation
The documentation should be checked for plagiarism and as per UGC guidelines, should be less than 10%.

1. Documentation Report (Chapter 1 to 4) 20
2. Innovation in the topic 10
3. Documentation/Topic presentation and viva voce 20

Project Implementation and Viva
Voce Evaluation
1. Documentation Report (Chapter 5 to last) 20
2. Implementation 10
3. Relevance of the topic 10
4. Viva Voce 10

Page 123

63




















Appendix – 1

Page 124

64 Project Documentation and Viva -voce (Semester III) and
Project Implementation and Viva -Voce (Semester IV)


Goals of the course Project Documentation and Viva -Voce

The student should:
 be able to apply relevant knowledge and abilities, within the main field of study, to a given problem
 within given constraints, even w ith limited information, independently analyse and discuss complex
inquiries/problems and handle larger problems on the advanced level within the main field of study
 reflect on, evaluate and critically review one’s own and others’ scientific results
 be abl e to document and present one’s own work with strict requirements on structure, format, and language
usage
 be able to identify one’s need for further knowledge and continuously develop one’s own knowledge

To start the project:
 Start thinking early in the programme about suitable projects.
 Read the instructions for the project.
 Attend and listen to other student´s final oral presentations.
 Look at the finished reports.
 Talk to senior master students.
 Attend possible information events (workshops / seminars / conferences etc.) about the related topics.

Application and approval:
 Read all the detailed information about project.
 Finalise finding a place and supervisor.
 Check with the coordinator about subject/project, place and supervisor.
 Write the project proposal and plan along with the supervisor.
 Fill out the application together with the supervisor.
 Hand over the complete application, proposal and plan to the coordinator.
 Get an acknowledgemen t and approval from the coordinator to start the project.


During the project :
 Search, gather and read information and literature about the theory.
 Document well the practical work and your results.
 Take part in seminars and the running follow -ups/supervision.
 Think early on about disposition and writing of the final report.
 Discuss your thoughts with the supervisor and others.
 Read the SOP and the rest you need again.
 Plan for and do the mid -term reporting to the coordinator/examiner.
 Do a mid -term report also at the work -place (can be a requirement in some work -places).
 Write the first draft of the final report and rewrite it based on feedback from the supervisor and possibly others.
 Plan for the final presentation of the report.

Finishing the project :
 Finish the report and obtain an OK from the supervisor.
 Ask the supervisor to send the certificate and feedback form to the coordinator.
 Attend the pre -final oral pr esentation arranged by the Coordinator.
 Rewrite the final report again based on feedback from the opponents and possibly others.
 Prepare a title page and a popular science summary for your report.

Page 125

65  Send the completed final report to the coordinator (via p lagiarism software)
 Rewrite the report based on possible feedback from the coordinator.
 Appear for the final exam.

Project Proposal /research plan
 The student should spend the first 1 -2 weeks writing a 1 -2 pages project plan containing:
- Short background of the project
- Aims of the project
- Short description of methods that will be used
- Estimated time schedule for the project
 The research plan should be handed in to the supervisor and the coordinator.
 Writing the project plan will help you plan your project work and get you started in finding information and
understanding of methods needed to perform the project .

Project Documentation
The documentation should contain:
 Introduction - that should contain a technical and social (when possible) motivation of the project topic .
 Description of the problems/topics.
 Status of the research/knowledge in the field and literature review.
 Description of the methodology/approach. (T he actual structure of the chapters here depends on the topic of the
documentation .)
 Results - must always contain analyses of results and associated uncertainties.
 Conclusions and proposals for the future work.
 Appendices (when needed).
 Bibliography - references and links.

For the master’s documentation, the chapters cannot be dictated, they may vary according to the type of
project. However, in Semester III Project Documentation and Viva Voce must contain at least 4 chapters
(Introduction, Review of Lite rature, Methodology / Approach, Proposed Design / UI design, etc. depending
on the type of project.) The Semester III report should be spiral bound.

In Semester IV, the remaining Chapters should be included (which should include Experiments performed,
Results and discussion, Conclusions and proposals for future work, Appendices) and Bibliography -
references and links . Semester IV report should include all the chapters and should be hardbound.

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2

AC – 11/07/2022
ItemNo. 6.13 (3) (R)




































UNIVERSITY OF MUMBAI



Revised Syllabus for

M.Sc. IT (Cloud Computing)

PartI I (Semester I to IV)
(Choice Based Credit System)




(With effect from the academic year 2022 -2023)

Page 127

3

Page 128

4

Semester –I
Course Code Course Title Credits
PSIT101 Researchin Computing 4
PSIT102 Data Science 4
PSIT103 Cloud Computing 4
PSIT104 SoftComputing Techniques 4
PSIT1P1 ResearchinComputing Practical 2
PSIT1P2 DataScience Practical 2
PSIT1P3 CloudComputing Practical 2
PSIT1P4 SoftComputingTechniques Practical 2
Total Credits 24


Semester –II
Course Code Course Title Credits
PSIT201 BigData Analytics 4
PSIT202 Modern Networking 4
PSIT203 Microservices Architecture 4
PSIT204 Image Processing 4
PSIT2P1 BigDataAnalytics Practical 2
PSIT2P2 ModernNetworking Practical 2
PSIT2P3 MicroservicesArchitecture Practical 2
PSIT2P4 Image Processing Practical 2
Total Credits 24

Page 129

5 ProgramSpecific Outcomes
PSO1: Ability to applythe knowledge ofInformation Technology with recent trendsalignedwith
research and industry.

PSO2: Ability to apply IT in the field of Computational Research, Soft Computing, Big Data
Analytics, Data Science, Image Processing, Artificial Intelligence, Networking and Cloud
Computing.

PSO3: Ability to provide socially acceptable technical solutions in the domains of Information
Security,MachineLearning,InternetofThingsandEmbeddedSystem,InfrastructureServicesas
specializations.

PSO4: Ability to apply the knowledge of Intellectual Property Rights, Cyber Laws and Cyber
Forensics and various standards in interest of National Security and Integrity along with IT
Industry.

PSO5: Ability to write effective project reports, research publications and content development
and to work in multidisciplinary environment in the context of changing technologies.

Page 130

6




















SEMESTER I

Page 131

7  Tobeabletoconductbusinessresearchwithanunderstandingofall the
latest theories.
 Todeveloptheabilitytoexploreresearchtechniquesusedforsolving any
real world or innovate problem. Objectives
Basicknowledgeof statisticalmethods.Analyticalandlogical thinking. Pre requisites M.Sc(Information Technology) Semester – I
CourseName: Researchin Computing CourseCode: PSIT101
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40





Unit Details Lectures
I Introduction: Role of Business Research, Information Systems and
Knowledge Management, Theory Building, Organization ethics and
Issues
12
II Beginning Stages of Research Process: Problem definition,
Qualitative research tools, Secondary data research 12
III Research Methods and Data Collection: Survey research,
communicatingwithrespondents,Observationmethods, Experimental
research
12
IV Measurement Concepts, Samplingand Field work: Levelsof Scale
measurement, attitude measurement, questionnaire design, sampling
designs and procedures, determination of sample size
12
V Data Analysis and Presentation: Editing and Coding, Basic Data
Analysis, Univariate Statistical Analysis and Bivariate Statistical
analysis and differences between two variables. Multivariate Statistical
Analysis.
12


Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. BusinessResearch Methods William
G.Zikmund, B.J
Babin,J.C. Carr, Cengage 8e 2016

Page 132

8
Alearnerwillbeable to:
solve real world problems with scientific approach.
developanalyticalskillsbyapplyingscientificmethods.
recognize,understandandapplythelanguage,theoryandmodelsof the
field of business analytics
fosteranabilityto criticallyanalyze,synthesizeandsolvecomplex
unstructured business problems
understandandcriticallyapplytheconceptsandmethodsof business
analytics
identify,modelandsolvedecisionproblemsindifferentsettings
interpret results/solutions and identify appropriate courses of
action for a given managerial situation whether a problem or an
opportunity
createviablesolutionstodecisionmaking problems
Course Outcome AtanuAdhikari,
M.Griffin
2. Business
Analytics Albright
Winston Cengage 5e 2015
3. ResearchMethods for
BusinessStudentsFifth
Edition Mark Saunders 2011
4. MultivariateData Analysis Hair Pearson 7e 2014

M.Sc(Information Technology) Semester – I
CourseName: ResearchinComputing Practical CourseCode: PSIT1P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hou
rs Marks
Evaluation System Practical Examination 2 40



Practical No Details
1 - 10 10Practicalbasedon abovesyllabus,covering entire syllabus

Page 133

9
Developindepthunderstandingofthekeytechnologiesindatascience and
business analytics: data mining, machine learning, visualization
techniques, predictive modeling, and statistics.
Practiceproblemanalysisanddecision -making.
Gain practical, hands -on experience with statistics programming
languages andbig data tools
throughcourseworkandappliedresearch experiences. Objectives
Basicunderstandingof statistics Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Data Science CourseCode: PSIT102
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Data Science Technology Stack: Rapid Information Factory
Ecosystem, Data Science Storage Tools, Data Lake, Data Vault, Data
WarehouseBusMatrix,DataScience ProcessingTools,Spark,Mesos,
Akka,Cassandra,Kafka,ElasticSearch,R,Scala,Python,MQTT,The
Future
Layered Framework: Definition of Data Science Framework, Cross -
Industry Standard Process for Data Mining (CRISP -DM),
Homogeneous Ontology for Recursive Uniform Schema, The Top
Layers of a Layered Framework, Layered Framework for High -Level
Data Science and Engineering
Business Layer: Business Layer, Engineering a Practical Business
Layer
Utility Layer: Basic Utility Design, Engineering a Practical Utility
Layer




12
II Three Management Layers: Operational Management Layer,
Processing -Stream Definition and Management, Audit, Balance, and
Control Layer, Balance, Control, Yoke Solution, Cause -and-Effect,
Analysis System, Functional Layer, Data Science Process
Retrieve Superstep : Data Lakes, Data Swamps, Training the Trainer
Model, Understanding the Business Dynamics of the Data Lake,
Actionable Business Knowledge from Data Lakes, Engineering a
Practical Retrieve Superstep, Connecting to Other Data Sources,

12
III AssessSuperstep: AssessSuperstep,Errors,AnalysisofData, Practical
Actions, Engineering a Practical Assess Superstep, 12

Page 134

10  Apply quantitative modeling and data analysis techniques to the
solution of real world business problems, communicate findings, and
effectively present results using data visualization techniques.
 Recognize and analyze ethical issues in businessrelated to intellectual
property, data security, integrity, and privacy. Course Outcome IV Process Superstep : Data Vault, Time -Person -Object -Location -Event
Data Vault, Data Science Process, Data Science,
Transform Superstep : Transform Superstep, Building a Data
Warehouse, Transforming with Data Science, Hypothesis Testing,
Overfitting and Underfitting, Precision -Recall, Cross -Validation Test.
12
V Transform Superstep: Univariate Analysis, Bivariate Analysis,
Multivariate Analysis, Linear Regression, Logistic Regression,
Clustering Techniques, ANOVA, Principal Component Analysis
(PCA),DecisionTrees,SupportVectorMachines,Networks,Clusters, and
Grids, Data Mining, Pattern Recognition, Machine Learning, Bagging
Data,Random Forests, Computer Vision (CV) , Natural Language
Processing (NLP), Neural Networks, TensorFlow.
Organize and Report Supersteps : Organize Superstep, Report
Superstep, Graphics, Pictures, Showing the Difference


12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. PracticalData Science AndreasFrançois
Vermeulen APress 2018
2. PrinciplesofData Science Sinan Ozdemir PACKT 2016
3. DataSciencefrom Scratch Joel Grus O’Reilly 2015
4. DataSciencefromScratch first
Principle in python Joel Grus Shroff
Publishers 2017
5. ExperimentalDesignin
Datasciencewith Least
Resources NCDas Shroff
Publishers 2018


M.Sc(Information Technology) Semester – I
CourseName: DataScience Practical CourseCode: PSIT1P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 135

11  Applyethicalpracticesineverydaybusinessactivitiesandmakewell -
reasoned ethical business and data management decisions.
 Demonstrateknowledgeofstatistical dataanalysistechniquesutilized in
business decision making.
 ApplyprinciplesofDataSciencetotheanalysisofbusiness problems.
 Usedataminingsoftwaretosolvereal -world problems.
 Employcuttingedgetoolsand technologiestoanalyzeBig Data.
 Applyalgorithmstobuildmachine intelligence.
 Demonstrateuseofteamwork,leadershipskills,decisionmakingand
organization theory.

Page 136

12
TolearnhowtouseCloudServices. To
implement Virtualization.
To implement Task Scheduling algorithms.
Apply Map-Reduceconcepttoapplications.
To build Private Cloud.
Broadlyeducatetoknowtheimpactofengineeringonlegaland
societal issues involved. Objectives M.Sc(Information Technology) Semester – I
CourseName:Cloud Computing CourseCode: PSIT103
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Cloud Computing: Introduction, Historical
developments, BuildingCloudComputingEnvironments, Principlesof
ParallelandDistributedComputing: ErasofComputing,Parallelv/s
distributed computing, Elements of Parallel Computing, Elements of
distributed computing, Technologies for distributed computing.
Virtualization: Introduction, Characteristics of virtualized
environments, Taxonomy of virtualization techniques, Virtualization
and cloud computing, Pros and cons of virtualization, Technology
examples.LogicalNetworkPerimeter,VirtualServer,Cloud Storage
Device,Cloudusagemon itor,Resourcereplication, Ready -made
environment.



12
II CloudComputingArchitecture: Introduction,Fundamentalconcepts
andmodels,Rolesandboundaries,CloudCharacteristics,Cloud Delivery
models, Cloud Deployment models, Economics of the cloud,
Open challenges. FundamentalCloudSecurity: Basics,Threat
agents,Cloudsecuritythreats,additionalconsiderations. Industrial
Platforms and New Developments: Amazon Web Services,Google
App Engine, Microsoft Azure.

12
III Specialized Cloud Mechanisms: Automated Scaling listener, Load
Balancer, SLA monitor, Pay -per-use monitor, Audit monitor, fail over
system, Hypervisor, Resource Centre, Multidevice broker, State
Management Database. Cloud Management Mechanisms: Remote
administration system, Resource Ma nagement System, SLA
Management System, Billing Management System, Cloud Security
Mechanisms: Encryption, Hashing, Digital Signature, PublicKey
Infrastructure(PKI),IdentityandAccessManagement(IAM), Single

12

Page 137

13 Sign-On(SSO),Cloud -Based SecurityGroups,HardenedVirtual Server
Images
IV Fundamental Cloud Architectures: Workload Distribution
Architecture, Resource Pooling Architecture, Dynamic Scalability
Architecture, Elastic Resource Capacity Architecture, Service Load
Balancing Architecture, Cloud Bursting Architecture, Elastic Disk
ProvisioningArchitecture,RedundantStorageArchitecture. Advanced
Cloud Architectures: Hypervisor Clustering Architecture, Load
Balanced Virtual Server Instances Architecture, Non -Disruptive
Service Relo cation Architecture, Zero Downtime Architecture, Cloud
Balancing Architecture, Resource Reservation Architecture, Dynamic
Failure DetectionandRecoveryArchitecture,Bare -Metal Provisioning
Architecture,RapidProvisioningArchitecture,StorageWorkload
Management Architecture



12
V CloudDelivery ModelConsiderations: CloudDeliveryModels:The
CloudProviderPerspective,CloudDeliveryModels:TheCloud Consumer
Perspective, Cost Metrics and Pricing Models : Business Cost
Metrics, Cloud Usage Cost Metrics, Cost Management
Considerations, Service Quality Metrics and SLAs: Service Quality
Metrics, SLA Guidelines

12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Mastering
Cloud ComputingFoundatio
nsand Applications
Programming RajkumarBuyya,
Christian
Vecchiola, S.
Thamarai Selvi Elsevier - 2013
2. Cloud Computing
Concepts,Technology& Arc
hitecture ThomasErl,
Zaigham
Mahmood,
andRicardo
Puttini Prentice
Hall - 2013
3. Distributed and Cloud
Computing, From Parallel
ProcessingtotheInternetof
Things Kai Hwang, Jack
Dongarra,Geoffrey
Fox MK
Publishers -- 2012

Page 138

14  AnalyzetheCloudcomputingsetupwithitsvulnerabilitiesand
applications using different architectures.
 Designdifferentworkflowsaccordingtorequirementsandapply
map reduce programming model.
 ApplyanddesignsuitableVirtualizationconcept,CloudResource
Management and design scheduling algorithms.
 Createcombinatorialauct ionsforcloudresourcesanddesign
scheduling algorithms for computing clouds
 AssesscloudStoragesystemsandCloudsecurity,therisks
involved, its impact and develop cloud application
 Broadly educate to know the impact of engineering on legal and
societalissuesinv olvedinaddressingthesecurityissues ofcloud
computing. Course Outcome M.Sc(Information Technology) Semester – I
CourseName:CloudComputing Practical CourseCode: PSIT1P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 139

15 problem Allthesetechniqueswillbemoreeffectivetosolvethe efficiently • • Softcomputingconceptslikefuzzylogic,neuralnetworksandgenetic
algorithm, where Artificial Intelligence is mother branch of all. Objectives
BasicconceptsofArtificialIntelligence.Knowledgeof Algorithms Pre requisites M.Sc(Information Technology) Semester – I
CourseName:SoftComputing Techniques CourseCode: PSIT104
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction of soft computing, soft computing vs. hard computing,
various types of soft computing techniques, Fuzzy Computing, Neural
Computing, Genetic Algorithms, Associative Memory, Adaptive
Resonance Theory, Classification, Clustering, Bayesian Networks,
Probabilistic reasoning, applications of soft computing.

12
II Artificial Neural Network: Fundamental concept, Evolution of Neural
Networks, Basic Models, McCulloh -Pitts Neuron, Linear Separability,
Hebb Network.
Supervised Learning Network: Perceptron Networks, Adaptive Linear
Neuron,MultipleAdaptiveLinearNeurons,BackpropagationNetwork,
Radial BasisFunction,TimeDelayNetwork,FunctionalLinkNetworks,
Tree Neural Network.
Associative Memory Networks: Training algorithm for pattern
Association, Autoassociative memory network, hetroassociative
memory network, bi -directional associative memory, Hopfield
networks, iterative autoassociative memory networks, temporal
associative memory networks.



12
III UnSupervised Learning Networks: Fixed weight competitive nets,
Kohonen self-organizing feature maps, learning vectors quantization,
counter propogation networks, adaptive resonance theory networks.
Special Networks: Simulated annealing, Boltzman machine, Gaussian
Machine,CauchyMachine,Probabilisticneuralnet,cascadecorrelatio n
network, cognition network, neo -cognition network, cellular neural
network, optical neural network
ThirdGenerationNeural Networks:
SpikingNeuralnetworks,convolutionalneuralnetworks,deeplearning
neural networks, extreme learning machine model.



12

Page 140

16 IV IntroductiontoFuzzyLogic,ClassicalSetsandFuzzysets: Classical
sets, Fuzzy sets.
ClassicalRelationsandFuzzy Relations:
CartesianProductofrelation,classicalrelation,fuzzyrelations,
tolerance and equivalence relations, non -iterative fuzzy se ts.
Membership Function: features of the membership functions,
fuzzification, methods of membership value assignments.
Defuzzification:Lambda -cutsforfuzzysets,Lambda -cutsforfuzzy
relations, Defuzzification methods.
FuzzyArithmeticandFuzzymeasures:fuzzy arithmetic,fuzzy
measures, measures of fuzziness, fuzzy integrals.



12
V FuzzyRulebaseandApproximate reasoning:
Fuzzy proportion, formation of rules, decomposition of rules,
aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems,
Fuzzy logic control systems, control system design, architecture and
operation of FLC system, FLC system models and applicat ions of FLC
System.
Genetic Algorithm: Biological Background, Traditional optimization
and search techniques, genetic algorithm and search space, genetic
algorithmvs.traditionalalgorithms,basicterminologies,simplegenetic
algorithm, general genetic algorith m, operators in genetic algorithm,
stopping condition for genetic algorithm flow, constraints in genetic
algorithm, problem solving using genetic algorithm, the schema
theorem,classificationofgeneticalgorithm,Hollandclassifiersystems,
genetic programming, advantages and limitations and applications of
genetic algorithm.
Differential Evolution Algorithm, Hybrid soft computing techniques –
neuro –fuzzyhybrid,geneticneuro -hybridsystems,genetic fuzzy
hybrid and fuzzy genetichybrid systems.





12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. ArtificialIntelligenceand Soft
Computing Anandita Das
Battacharya SPD 3rd 2018
2. PrinciplesofSoft computing S.N.SivanandamS
.N.Deepa Wiley 3rd 2019
3. Neuro -Fuzzy and Soft
Computing J.S.R.Jang,
C.T.Sunand
E.Mizutani Prentice
Hall of
India 2004
4. NeuralNetworks,Fuzzy
Logic and Genetic
Algorithms:Synthesis& Appli
cations S.Rajasekaran,
G.A.
Vijayalakshami Prentice
Hall of
India 2004
5. Fuzzy Logic with
EngineeringApplications Timothy J.Ross McGraw -
Hill 1997

Page 141

17 • Identifyanddescribesoftcomputingtechniquesandtheirrolesin
building intelligent machines
• Recognizethefeasibilityofapplyingasoftcomputingmethodology for
a particular problem
• Applyfuzzylogicandreasoningtohandleuncertaintyandsolve
engineering problems
• Applygeneticalgorithmstocombinatorialoptimization problems
• Applyneuralnetworksforclassificationandregression problems
• Effectivelyuseexistingsoftwaretoolstosolverealproblemsusing a
soft computing approach
• Evaluateandcomparesolutionsby varioussoftcomputing
approaches for a given problem. Course Outcome 6. GeneticAlgorithms: Search,
OptimizationandMachine
Learning Davis
E.Goldberg Addison
Wesley 1989
7. IntroductiontoAIand
Expert System Dan W.
Patterson Prentice
Hallof
India 2009

M.Sc(Information Technology) Semester – I
CourseName:SoftComputingTechniques Practical CourseCode: PSIT1P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus, coveringentire syllabus

Page 142

18

















SEMESTER II

Page 143

19  Toprovideanoverviewofanexcitinggrowingfieldofbigdata analytics.
 Tointroducethetoolsrequiredtomanageandanalyzebigdatalike
Hadoop, NoSql MapReduce.
 Toteachthefundamentaltechniquesandprinciplesinachievingbigdata
analytics with scalability and streaming capability.
 Toenablestudentstohaveskillsthatwillhelpthemtosolvecomplexreal - world
problems in for decision support. Objectives M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics CourseCode: PSIT201
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Introduction to Big Data, Characteristics of Data, and Big Data
Evolution of Big Data, Definition of Big Data, Challenges with big
data, Why Big data? Data Warehouse environment, Traditional
Business Intelligence versus Big Data. State of Practice in Analy tics,
Key roles for New Big Data Ecosystems, Examples of big Data
Analytics.
BigDataAnalytics,Introductiontobigdataanalytics,Classificationof
Analytics, Challenges of Big Data, Importance of Big Data, Big Data
Technologies, Data Science, Responsibilities, Soft state eventual
consistency. Data Analytics Life Cycle


12
II Analytical Theory and Methods: Clustering and Associated
Algorithms, Association Rules, Apriori Algorithm, Candidate Rules,
ApplicationsofAssociationRules,Validationand Testing,
Diagnostics,Regression,LinearRegression,LogisticRegression,
Additional Regression Models.
12
III Analytical Theory and Methods: Classification, Decision Trees, Naïve
Bayes, Diagnostics of Classifiers, Additional Classification Methods,
TimeSeries Analysis,BoxJenkinsmethodology,ARIMAModel,
Additionalmethods.TextAnalysis,Steps,TextAnalysisExample,
Collecting Raw Text, Representing Text, Term Frequency -Inverse
DocumentFrequency(TFIDF),CategorizingDocumentsbyTopics,
Determining Sentiments

12
IV Data Product, Building Data Products at Scale with Hadoop, Data
Science Pipeline and Hadoop Ecosystem, Operating System for Big
Data, Concepts, Hadoop Architecture, Working with Distributed file
system,WorkingwithDistributedComputation,Frameworkfor Python
andHadoopStreaming,HadoopStreaming,MapReducewith Python,
12

Page 144

20 applications etc. Understand the key issues in big data management and its
associated applications in intelligent business and scientific
computing.
Acquire fundamental enabling techniques and scalable
algorithms like Hadoop, Map Reduce and NO SQL in bi g data
analytics.
Interpret business models and scientific computing paradigms,
and apply software tools for big data analytics.
Achieve adequate perspectives of big data analytics in various
applicationslikerecommendersystems,social media •


•


•

• Course Outcome Advanced MapReduce. In -Memory Computing with Spark, Spark
Basics,InteractiveSparkwithPySpark,WritingSparkApplications,
V Distributed Analysis and Patterns, Computing with Keys, Design
Patterns, Last -Mile Analytics, Data Mining and Warehousing,
StructuredDataQuerieswithHive,HBase,DataIngestion, Importing
RelationaldatawithSqoop,Injestingstreamdatawithflume.Analytics
with higher level APIs, Pig, Spark’s higher level APIs.
12
,
Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Big Data and Analytics Subhashini
Chellap panSee
maAcharya Wiley First
2. DataAnalyticswith Hadoop
AnIntroductionforData
Scientists Benjamin
Bengfortand
Jenny Kim O’Reilly 2016
3. Big Data and Hadoop V.KJain Khanna
Publishing First 2018

M.Sc(Information Technology) Semester – II
CourseName:BigData Analytics Practical CourseCode: PSIT2P1
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 145

21  Tounderstandthestate -of-the-artinnetworkprotocols,architecturesand
applications.
 Analyzeexistingnetworkprotocolsand networks.
 Developnewprotocolsin networking
 Tounderstandhownetworkingresearchis done
 ToinvestigatenovelideasintheareaofNetworkingvia term-longresearch
projects. Objectives
Fundamentalsof Networking Pre requisites M.Sc(Information Technology) Semester – I
CourseName:Modern Networking CourseCode: PSIT202
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Modern Networking
ElementsofModern Networking
The Networking Ecosystem ,Example Network Architectures,Global
Network Architecture,A Typical Network Hierarchy Ethernet
Applications of Ethernet Standards Ethernet Data Rates Wi -Fi
ApplicationsofWi -Fi,StandardsWi -FiDataRates4G/5GCellularFirst
Generation Second Generation, Third Generation Fourth Generation
Fifth Generation, Cloud Computin g Cloud Computing Concepts The
Benefits of Cloud Computing Cloud Networking Cloud Storage,
Internet of Things Things on the Internet of Things, Evolution Layers
of the Internet of Things, Network Convergence Unified
Communications, Requirements and Technol ogy Types of Network
andInternetTraffic,ElasticTraffic,InelasticTraffic,Real -TimeTraffic
Characteristics Demand: Big Data, Cloud Computing, and Mobile
TrafficBig Data Cloud Computing,,Mobile Traffic, Requirements:
QoS and QoE,,Quality of Service,Quality of Experience, Routing
Characteristics, Packet Forwarding, Congestion Control ,Effects of
Congestion,CongestionControlTechniques,SDNandNFV Software -
DefinedNetworking,NetworkFunctionsVirtualizationModern
Networking Elements







12
II Software -Defined Networks
SDN: Background and Motivation, Evolving Network Requirements
Demand Is Increasing,Supply Is IncreasingTraffic Patterns Are More
ComplexTraditionalNetworkArchitecturesareInadequate,The SDN
ApproachRequirementsSDNArchitectureCharacteristicsof Software -

12

Page 146

22 Defined Networking, SDN - and NFV -Related Standards Standards -
Developing Organizations Industry Consortia Open Development
Initiatives, SDN Data Plane and OpenFlow SDN Data Plane, Data
Plane Functions Data Plane Protocols OpenFlow Logical Network
DeviceFlowTableStructureFlowTablePipeline,TheUseofMultiple
Tables Group Table OpenFlow Protocol, SDN Control Plane
SDNControlPlaneArchitectureControlPlaneFunctions,Southbound
Interface Northbound InterfaceRouting, ITU -T Model,
OpenDaylightOpenDaylightArchitectureOpenDaylightHelium,RESTR
ESTConstraintsExampleRESTAPI,CooperationandCoordination
Among Controllers, Centralized Versus DistributedControllers, High -
AvailabilityClustersFederatedSDNNetworks,BorderGateway
ProtocolRoutingan dQoSBetweenDomains,UsingBGPforQoS
ManagementIETFSDNiOpenDaylightSNDi SDNApplication Plane
SDNApplicationPlaneArchitectureNorthboundInterfaceNetwork
ServicesAbstractionLayerNetworkApplications,UserInterface,
NetworkServicesAbstractionLayerAbstractionsinSDN, Frenetic Traffic
Engineering PolicyCop Measurement and Monitoring Security
OpenDaylight DDoS Application Data Center Networking, Big Data
overSDNCloudNetworkingoverSDNMobilityandWireless
Information -Centric Networking CCNx, Use of an Abstraction Layer
III Virtualization, Network Functions Virtualization: Concepts and
Architecture, Background and Motivation for NFV, Virtual Machines
The Virtual Machine Monitor, Architectural Approaches Container
Virtualization, NFV Concepts Simple Example of the Use of NFV,
NFV Principles High -Level NFV Framework, NFV Benefits and
RequirementsNFVBenefits,NFVRequirements, NFV Reference
Architecture NFV Management and Orchestration, Reference Points
Implementation, NFV Functionality, NFV
Infrastructure,ContainerInterface ,Deployment of NFVI
Containers,Logical Structure of NFVI Domains,ComputeDomain,
Hypervisor Domain,Infrastructure Network
Domain, Virtualized Network Functions, VNF
Interfaces,VNFC to VNFC Communication,VNF Scaling, NFV
Management and Orchestration, Virtualized Infrastructure
Manager,Virtual Network Function Manager,NFV Orchestrator,
Repositories, Element Management, OSS/BSS, NFV Use Cases
Architectural Use Cases, Service -Oriented Use Cases, SDN and NFV
Network Virtualization, Virtual LANs ,The Use of Virtual
LANs,Defining VLANs, Communicating VLAN Membership,IEEE
802.1Q VLAN Standard, Nested VLANs, OpenFlow VLAN Support,
VirtualPrivateNetworks, IPsec VPNs,MPLS VPNs, Network
Virtualization, Simplified Example, Network Virtualization
Architecture, Benefits of Network Virtualization, OpenDaylight’s
Virtual Tenant Network, Software -Defined Infrastructure,Software -
DefinedStorage,SDI Architecture









12

Page 147

23 IV DefiningandSupportingUserNeeds,QualityofService,Background,
QoSArchitectural Framework,DataPlane,Control Plane,Management
Plane, Integrated Services Architecture, ISA Approach
ISA Components, ISA Services, Queuing Discipline, Differentiated
Services, Services, DiffServ Field, DiffServ Configuration and
Operation,Pe r-HopBehavior,DefaultForwardingPHB,ServiceLevel
Agreements, IP Performance Metrics, OpenFlow QoS Support, Queue
Structures, Meters, QoE: User Quality of Experience, Why
QoE?,Online Video Content Delivery, Service Failures Due to
InadequateQoEConsiderations QoE-RelatedStandardizationProjects,
Definition of Quality of Experience, Definition of Quality, Definition
of Experience Quality Formation Process, Definition of Quality of
Experience, QoE Strategies in Practice, The QoE/QoS Layered Model
Summarizing and M erging the ,QoE/QoS Layers, Factors Influencing
QoE, Measurements of QoE, Subjective Assessment, Objective
Assessment, End -User Device Analytics, Summarizing the QoE
Measurement Methods, Applications of QoE Network Design
Implications of QoS and QoE Classi fication of QoE/ QoS Mapping
Models, Black -Box Media -Based QoS/QoE Mapping Models, Glass -
BoxParameter -BasedQoS/QoEMappingModels,Gray -BoxQoS/QoE
Mapping Models, Tips for QoS/QoE Mapping Model Selection,IP -
Oriented Parameter -Based QoS/QoE Mapping Models,Ne twork Layer
QoE/QoS Mapping Models for Video Services, Application Layer
QoE/QoS Mapping Models for Video Services Actionable QoE over
IP-Based Networks, The System -Oriented Actionable QoE Solution,
The Service -Oriented Actionable QoE Solution, QoE Versus QoS
Service Monitoring, QoS Monitoring Solutions, QoE Monitoring
Solutions,QoE -BasedNetworkandServiceManagement,QoE -Based
Management of VoIP Calls, QoE -Based Host -Centric Vertical
Handover, QoE -Based Network -Centric Vertical Handover












12
V Modern Network Architecture: Clouds and Fog, Cloud Computing,
Basic Concepts, Cloud Services, Software as a Service, Platform as a
Service,InfrastructureasaService,OtherCloudServices,XaaS,Cloud
Deployment Models, Public Cloud Private Cloud Community Cloud,
Hybrid Cloud, Cloud Architecture, NIST Cloud Computing Reference
Architecture,ITU -T Cloud Computing Reference Architecture, SDN and
NFV, Service Provider Perspective Private Cloud Perspective, ITU -T
Cloud Computing Functional Reference Architecture, The I nternet of
Things: Components The IoT Era Begins, The Scope of the Internet of
Things Components of IoT -Enabled Things, Sensors, Actuators,
Microcontrollers, Transceivers, RFID, The Internet of Things:
Architecture and Implementation, IoT Architecture,ITU -T IoT
Reference Model, IoT World Forum Reference Model, IoT
Implementation, IoTivity, Cisco IoT System, ioBridge, Security
Security Requirements, SDN Security Threats to SDN, Software -
Defined Security, NFV Security, Attack Surfaces, ETSI Security
Perspect ive,SecurityTechniques,CloudSecurity,SecurityIssues and
Concerns,CloudSecurityRisksandCountermeasures,Data Protection






12

Page 148

24  Demonstratein -depth knowledgeintheareaofComputer Networking.
 To demonstratescholarship of knowledge through performing in agroup
to identify, formulate and solve a problem related to ComputerNetworks
 Prepare a technical document for the identified Networking System
Conducting experiments to analyze the identified research work in
building Computer Networks Course Outcome intheCloud,Cloud SecurityasaService,AddressingCloudComputer
SecurityConcerns,IoTSecurity,ThePatching Vulnerability, IoT
SecurityandPrivacyRequirementsDefinedbyITU -TAnIoT Security
Framework, Conclusion

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Foundationsof Modern
Networking:SDN,NFV,
QoE, IoT, and Cloud William
Stallings Addison -
Wesley
Professional October
2015
2. SDNandNFVSimplified A
Visual Guide to
Understanding Software
Defined Networks and
NetworkFunction
Virtualization Jim Doherty Pearson
Education,
Inc
3. Network Functions
Virtualization(NFV)
withaTouchof SDN Rajendra
Chayapathi
SyedFarrukh
Hassan Addison -
Wesley
4. CCIEandCCDEEvolving
Technologies Study
Guide Braddgeworth,
Jason Gooley,
RamiroGarza
Rios Pearson
Education,
Inc 2019

M.Sc(Information Technology) Semester – II
CourseName:ModernNetworking Practical CourseCode: PSIT2P2
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 149

25
Gainathoroughunderstandingofthephilosophyandarchitectureof Web
applications using ASP.NET Core MVC;
Gainapracticalunderstandingof.NET Core;
AcquireaworkingknowledgeofWebapplicationdevelopmentusing
ASP.NET Core MVC 6 and Visual Studio
PersistdatawithXMLSerializationandADO.NETwithSQLServer Create
HTTP services using ASP.NET Core Web API;
DeployASP.NETCoreMVCapplicationstotheWindows Azure
cloud. Objectives M.Sc(Information Technology) Semester – I
CourseName:Microservice Architecture CourseCode: PSIT203
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40




Unit Details Lectures
I Microservices: Understanding Microservices, Adopting
Microservices, The Microservices Way. Microservices Value
Proposition: DerivingBusinessValue,definingaGoal -
Oriented,LayeredApproach,Applyingthe Goal -
Oriented,LayeredApproach. Designing Microservice Systems:
The Systems Approach to Microservices, A
Microservices Design Process, Establishing a Foundation: Goals
and Principles, Platforms, Culture.

12
II Service Design: Microservice Boundaries, API design for
Microservices, Data and Microservices, Distributed Transactions and
Sagas, Asynchronous Message -Passing and Microservices, dealing
with Dependencies, System Design and Operations: Independent
Deployability, More Serv ers, Docker and Microservices, Role of
ServiceDiscovery,NeedforanAPIGateway,MonitoringandAlerting.
AdoptingMicroservicesinPractice: SolutionArchitecture Guidance,
OrganizationalGuidance,CultureGuidance,ToolsandProcess
Guidance, Services Guidance.


12
III Building Microservices with ASP.NET Core: Introduction,
Installing .NET Core, Building a Console App, Building ASP.NET
Core App. Delivering Continuously: Introduction to Docker,
Continuous integration with Wercker, Continuous Integration with
Circle CI, De ploying to Dicker Hub. Building Microservice with
ASP.NETCore: Microservice,TeamService,APIFirstDevelopment,
Test First Controller, Creating a CI pipeline, Integration Testing,
RunningtheteamserviceDockerImage. Backing Services:


12

Page 150

26 MicroservicesEcosystems,BuildingthelocationService,Enhancing
Team Service.
IV Creating Data Service: Choosing a Data Store , Building a Postgres
Repository, Databases are Backing Services, Integration Testing Real
Repositories, Exercise the Data Service. Event Sourcing and CQRS:
Event Sourcing, CQRS pattern, Event Sourcing and CQRS, Running
the samples. Building an ASP.NET Core Web Application:
ASP.NET Core Basics, Building Cloud -Native Web Applications.
ServiceDiscovery: CloudNativeFactors,NetflixEureka, Discovering
and Advertising ASP.NET Core Services. DNS and Platform Supported
Discovery.


12
V Configuring Microservice Ecosystems: Using Environment
VariableswithDocker,UsingSpringCloudConfigServer,Configuring
Microservices with etcd, Securing Applications and Microservices:
Security in the Cloud, Securing ASP.NET Core Web Apps, Securing
ASP.NET Core Microservices. Building Real -Time Apps and
Services: Real-Time Applications Defined, Websockets in the Cloud,
Using a Cloud Messaging Prov ider, Building the Proximity Monitor.
Putting It All Together: Identifying and Fixing Anti -Patterns,
Continuing the Debate over Composite Microservices, The Future.


12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. Microservice Architecture:
Aligning Principles,
Practices, and Culture Irakli
Nadareishvili,
Ronnie Mitra,
MattMcLarty,
and Mike
Amundsen O’Reilly First 2016
2. BuildingMicroserviceswith
ASP.NET Core Kevin Hoffman O’Reilly First 2017
3. Building Microservices:
DesigningFine -Grained
Systems SamNewman O’Reilly First
4. Production -ready
Microservices SusanJ. Fowler O’Reilly 2016

Page 151

27
DevelopwebapplicationsusingModelView Control.
CreateMVCModelsandwritecodethatimplementsbusinesslogic
within Model methods, properties, and events.
CreateViewsinanMVCapplicationthatdisplay andeditdataand interact
with Models and Controllers.
Boostyourhireabilitythroughinnovativeandindependent learning.
Gaining a thorough understanding of the philosophy and
architecture of .NET Core
Understanding packages, metapackages and frameworks
Acquirin gaworkingknowledgeofthe.NETprogrammingmodel
Implementing multi -threading effectively in .NET applications Course Outcome M.Sc (Information Technology) Semester – II
CourseName:MicroservicesArchitecture Practical CourseCode: PSIT2P3
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 152

28  Reviewthefundamentalconceptsofadigitalimageprocessing
system.
 Analyzeimagesinthefrequencydomainusingvarious transforms.
 Evaluatethetechniques forimageenhancementandimage restoration.
 Categorizevariouscompression techniques.
 InterpretImagecompression standards.
 Interpretimagesegmentationandrepresentation techniques. Objectives M.Sc(Information Technology) Semester – II
CourseName: Image Processing CourseCode: PSIT204
Periodsper week
1Periodis60 minutes Lectures 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Theory Internal -- 40



Unit Details Lectures
I Introduction: DigitalImageProcessing, OriginsofDigitalImageProcessing,
Applications and Examples of Digital Image Processing, Fundamental Steps
in Digital Image Processing, Components of an Image Processing System,
DigitalImageFundamentals: ElementsofVisualPerception,Lightandthe
Electromagneti c Spectrum, Image Sensing andAcquisition, Image Sampling
and Quantization, Basic Relationships Between Pixels, Basic Mathematical
Tools Used in Digital Image Processing, Intensity Transformations and
Spatial Filtering: Basics, Basic Intensity Transformatio n Functions, Basic
Intensity Transformation Functions, Histogram Processing, Fundamentals of
Spatial Filtering, Smoothing (Lowpass) Spatial Filters, Sharpening
(Highpass)SpatialFilters,Highpass,Bandreject,andBandpassFilters from
LowpassFilters,CombiningSpat ialEnhancementMethods,UsingFuzzy
Techniques for Intensity Transformations and Spatial Filtering




12
II Filtering in the Frequency Domain: Background, Preliminary Concepts,
Sampling and the Fourier Transform of Sampled Functions, The Discrete
Fourier Transform of One Variable, Extensions to Functions of Two
Variables, Properties of the 2 -D DFT and IDFT, Basics of Filtering in the
Frequency Do main, Image Smoothing Using Lowpass Frequency Domain
Filters, Image Sharpening Using Highpass Filters, Selective Filtering, Fast
Fourier Transform
Image Restoration and Reconstruction: A Model of the Image
Degradation/Restoration Process, Noise Models, Restoration in the Presence
of Noise Only -----Spatial Filtering, Periodic Noise Reduction Using
Frequency Domain Filtering, Linear, Position -Invariant Degradations,
EstimatingtheDegradationFunction,InverseFiltering,Minimum Mean
SquareError(Wiener)Filtering, ConstrainedLeastSquaresFiltering,
Geometric Mean Filter, Image Reconstruction from Projections




12
III WaveletandOtherImageTransforms: Preliminaries,Matrix -based
Transforms,Correlation,BasisFunctionsintheTime -FrequencyPlane, Basis 12

Page 153

29 Images, Fourier -Related Transforms, Walsh -Hadamard Transforms, Slant
Transform, Haar Transform, Wavelet Transforms
Color Image Processing: Color Fundamentals, Color Models, Pseudocolor
Image Processing, Full -Color Image Processing, Color Transformations,
ColorImageSmoothingandSharpening,UsingColorinImageSegmentation,
Noise in Color Images, Color Image Compression.
ImageCompressionandWatermarking: Fundamentals,HuffmanCoding,
GolombCoding,ArithmeticCoding,LZWCoding,Run -length Coding,
Symbol -basedCoding,8 Bit-planeCoding,BlockTransformCoding,
Predictive Coding, Wavelet Coding, Digital Image Watermarking,
IV Morphological Image Processing: Preliminaries, Erosion and Dilation,
Opening and Closing, The Hit -or-Miss Transform, Morphological
Algorithms, Morphol ogical Reconstruction¸ Morphological Operations on
Binary Images, Grayscale Morphology
ImageSegmentationI:EdgeDetection,Thresholding,andRegion Detection:
Fundamentals, Thresholding, Segmentation by Region Growing
andbyRegionSplittingandMerging,Region SegmentationUsingClustering and
Superpixels, Region Segmentation Using Graph Cuts, Segmentation Using
Morphological Watersheds, Use of Motion in Segmentation


12
V Image Segmentation II: Active Contours: Snakes and Level Sets:
Background, Image Segmentation Using Snakes, Segmentation Using Level
Sets.
Feature Extraction: Background, Boundary Preprocessing, Boundary
FeatureDescriptors,RegionFeatureDescriptors,PrincipalComponents as
FeatureDescriptors,Whole -ImageFeatures,Scale -InvariantFeature
Transform (SIFT)

12

Booksand References:
Sr.No. Title Author/s Publisher Edition Year
1. DigitalImage Processing Gonzalezand
Woods Pearson/Prentice
Hall Fourth 2018
2. FundamentalsofDigital
Image Processing AK.Jain PHI
3. TheImage Processing
Handbook J.C.Russ CRC Fifth 2010

M.Sc(Information Technology) Semester – II
CourseName:ImageProcessing Practical CourseCode: PSIT2P4
Periodsper week
1Periodis60 minutes Lectures 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 40

Practical No Details
1 - 10 10Practicalbasedonabovesyllabus,coveringentire syllabus

Page 154

30  Understandtherelevantaspectsofdigitalimagerepresentationand
their practical implications.
 Havetheabilitytodesignpointwiseintensitytransformations tomeet
stated specifications.
 Understand2 -Dconvolution,the2 -DDFT,andhavetheabitiltyto
design systems using these concepts.
 Haveacommandofbasicimagerestoration techniques.
 Understandtheroleofalternativecolorspaces,andthedesign
requirements leading to choices of color space.
 Appreciatetheutilityofwaveletdecompositionsandtheirroleinimage
processing systems.
 Haveanunderstandingoftheunderlyingmechanismsofimage
compression, and the ability to design systems usingstandard
algorithms to meet design specifications. Course Outcome

Page 155

31 Evaluation Scheme
InternalEvaluation(40 Marks)
Theinternalassessmentmarksshallbeawardedas follows:
1. 30marks(Anyoneofthe following):
a. WrittenTest or
b. SWAYAM(AdvancedCourse)ofminimum20hoursandcertificationexam
completed or
c. NPTEL(AdvancedCourse)ofminimum20hoursandcertificationexam
completed or
d. ValidInternationalCertifications(Prometric,Pearson,Certiport,Coursera,
Udemy and the like)
e. Onecertificationmarksshallbeawardedonecourseonly.Forfourcourses, the
students wil l have to complete four certifications.
2. 10 marks
Themarksgivenoutof40forpublishingtheresearchpapershouldbedividedinto four
course and should awarded out of 10 in each of the four course.

i. SuggestedformatofQuestionpaperof30marksforthewritten test.
Q1. Attempt anytwo ofthe following: 16
a.
b.
c.
d.

Q2. Attempt anytwo ofthe following: 14
a.
b.
c.
d.

ii. 10 marks from every course coming to a total of 40 marks, shall be awarded on
publishing of research paper in UGC approved Journal with plagiarism less than
10%.Themarkscanbeawardedaspertheimpactfactorofthejournal,qualityof the
paper, importance of the contents published, social value.

Page 156

32 ExternalExamination:(60
marks)


Allquestionsare compulsory
Q1 (BasedonUnit1)Attempt anytwo ofthe following: 12
a.
b.
c.
d.

Q2 (BasedonUnit2)Attempt anytwo ofthe following: 12
Q3 (BasedonUnit3)Attempt anytwo ofthe following: 12
Q4 (BasedonUnit4)Attempt anytwo ofthe following: 12
Q5 (BasedonUnit5)Attempt anytwo ofthe following: 12
PracticalEvaluation(50 marks)
ACertifiedcopyjournalisessentialtoappearforthepractical examination.

1. PracticalQuestion 1 20
2. PracticalQuestion 2 20
3. Journal 5
4. Viva Voce 5

OR

1. Practical Question 40
2. Journal 5
3. Viva Voce 5

Page 157

33




Academic Council _________
Item No: _________
















































University of Mumbai

Syllabus for M.Sc. I.T. Part II
Semester III and IV
Programme: M.Sc.
Subject: Information Technology
Specialized Degrees as
M.ScIT(Cloud Computing)
CHOICE BASED(REVISED)
with effect from the academic year
2023 – 2024

Page 158

34


Cloud Computing -Major

Semester III Compulsory Course

Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT301 Technical Writing and
Entrepreneurship
Development 60 4 PSIT3P1 60 2 6

Semester IV Compulsory Course
Paper code Paper
Lectures Credit Practical
Hrs Credit Total Credit
Nomenclature Paper
PSIT401 Blockchain 60 4 PSIT4P1 60 2 6

Semester III MSc IT with specialization in Image Processing [MSc IT(AI)]
Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT302c Cloud Application
Development 60 4 PSIT3P2c 60 2 6
PSIT303c Cloud Management 60 4 PSIT3P3c 60 2 6
PSIT304c Data Center Technologies 60 4 PSIT3P4c 60 2 6
Semester IV MSc IT with specialization in Image Processing [MSc IT(AI)]
Paper
code Paper
Lectures Credit Practical
Hrs Credit Total
Credit Nomenclature Paper
PSIT402c Advanced IoT 60 4 PSIT4P2c 60 2 6
PSIT403c Server Virtualization on
VMWare Platform 60 4 PSIT4P3c 60 2 6
PSIT404c Storage as a Service 60 4 PSIT4P4c 60 2 6

PSIT4P4 - Project Implementation and Viva

Page 159

35



















SEMESTER III

Page 160

36











PSIT301: Technical Writing and
Entrepreneurship Development
M. Sc (Information Technology) Semester – III
Course Name: Technical Writing and Entrepreneurship
Development Course Code: PSIT301
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:

 This course aims to provide conceptual understanding of developing strong foundation in general writing,
including research proposal and reports.
 It covers the technological developing skills for writing Article, Blog, E -Book, Commercial web Page
design, Business Listing Press Release, E -Listing and Product Description.
 This course aims to provide conceptual understanding of innovation and entrepreneurship develo pment.

Unit Details Lectures Outcome
I Introduction to Technical Communication:
What Is Technical Communication? The Challenges of
Producing Technical Communication, Characteristics of a
Technical Document , Measures of Excellence in Technical
Documents, Skills and Qualities Shared by Successful
Workplace Communicators, How Communication Skills and
Qualities Affect Your Career? Understanding Ethical and
Legal Considerations: A Brief Introduction to Ethics, Your
Ethical Obligations, Your Legal Obligations, The Role of
Corporate Culture in Ethical and Legal
Conduct,Understanding Ethical and Legal Issues Related to
Social Media, Communicating Ethically Across Cultures,
Principles for Ethical Communication Writing T echnical
Documents:
Planning, Drafting, Revising, Editing, Proofreading Writing
Collaboratively: Advantages and Disadvantages of
Collaboration, Managing Projects, Conducting Meetings,
Using Social Media and Other Electronic Tools in
Collaboration, Importance of Word Press Website, Gender and
Collaboration, Culture and Collaboration . 12 CO1
II Introduction to Content Writing: Types of Content (Article, 12 CO2

Page 161

37 Blog, E -Books, Press Release, Newsletters Etc), Exploring
Content Publication Channels. Distribution of your content
across various channels. Blog Creation: Understand the
psychology behind your web traffic, Creating killing landing
pages which attract users, Using Landing Page Creators,
Setting up Accelerated Mobile Pages, Identifying UI UX
Experience of your webs ite or blog. Organizing Your
Information: Understanding Three Principles for
Organizing Technical Information, Understanding
Conventional Organizational Patterns, Emphasizing
Important Information: Writing Clear, Informative Titles,
Writing Clear, Informative Headings, Writing Clear
Informative Lists, Writing Clear Informative Paragraphs .
III Creating Graphics: The Functions of Graphics, The
Characteristics of an Effective Graphic, Understanding the
Process of Creating Graphics, Using Color Effectively,
Choosing the Appropriate Kind of Graphic, Creating Effective
Graphics for Multicultural Readers. Researching Your
Subject: Understanding the Differences Between Academic
and WorkplaceResearch, Understanding the Research Process,
Conducting Secondary Researc h, Conducting Primary
Research, Research and Documentation: Literature
Reviews, Interviewing for Information, Documenting Sources,
Copyright, Paraphrasing, Questionnaires. Report
Components: Abstracts, Introductions, Tables of Contents,
Executive Summaries, Feasibility Reports, Investigative
Reports, Laboratory Reports, Test Reports, Trip Reports,
Trouble Reports 12 CO3
IV Writing Proposals: Understanding the Process of Writing
Proposals, The Logistics of Proposals, The ―Deliverables‖ of
Proposals, Persuasio n and Proposals, Writing a Proposal, The
Structure of the Proposal . Writing Informational Reports:
Understanding the Process of Writing Informational Reports,
Writing Directives, Writing Field Reports, Writing Progress
and Status Reports, Writing Incident Reports, Writing Meeting
Minutes . Writing Recommendation Reports: Understanding
the Role of Recommendation Reports, Using a Problem -
Solving Model for Preparing Recommendation Reports,
Writing Recommendation Reports . Reviewing, Evaluating,
and Testing Docume nts and Websites: Understanding
Reviewing, Evaluating, and Testing, Reviewing Documents
and Websites, Conducting Usability Evaluations, Conducting
Usability Tests, Using Internet tools to check writing Quality,
Duplicate Content Detector, What is Plagiaris m?, How to
avoid writing plagiarism content? Innovation management:
an introduction: The importance of innovation, Models of
innovation, Innovation as a management process . Market
adoption and technology diffusion: Time lag between
innovation and useable prod uct, Innovation and the market
,Innovation and market vision ,Analysing internet search data
to help adoption andforecasting sales ,Innovative new
products and consumption patterns, Crowdsourcing for new
product ideas, Frugal innovation and ideas from
everywhere,Innovation diffusion theories . 12 CO4
V Managing innovation within firms: Organisations and 12 CO5

Page 162

38 innovation, The dilemma of innovation management,
Innovation dilemma in low technology sectors, Dynamic
capabilities, Managing uncertainty, Managing innovation
projects Operations and process innovation: Operations
management, The nature of design and innovation in the
context of operations, Process design, Process design and
innovation
Managing intellectual property: Intellectual property, Trade
secret s, An introduction to patents, Trademarks, Brand names,
Copyright Management of research and development: What
is research and development?, R&D management and the
industrial context, R&D investment and company success,
Classifying R&D, R&D management and it s link with
business strategy, Strategic pressures on R&D, Which
business to support and how?, Allocation of funds to R&D,
Level of R&D expenditure Managing R&D
projects: Successful technology management, The changing
nature of R&D management, The acquisition of external
technology, Effective R&D management, The link with the
product innovation process, Evaluating R&D projects .

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Technical Communication
Mike Markel Bedford/St.
Martin's 11 2014
2. Innovation Management and
New Product Development Paul Trott Pearson 06 2017
3. Handbook of Technical
Writing
Gerald J.
Alred , Charles T.
Brusaw , Walter E.
Oliu Bedford/St.
Martin's 09 2008
4. Technical Writing 101: A
Real-World Guide to
Planning and Writing
Technical Content Alan S. Pringle and
Sarah S. O'Keefe scriptorium 03 2009
5. Innovation and
Entrepreneurship Peter Drucker Harper
Business 03 2009



Course Outcomes:
After completion of the course, a student should be able to:
CO1: Develop technical documents that meet the requirements with standard guidelines. Understanding the
essentials and hands -on learning about effective Website Development.
CO2: Write Better Quality Content Which Ranks faster at Search Engines. Build effective Social Media Pages.
CO3: Evaluate the essentials parameters of effective Social Media Pages.
CO4: Understand importance of innovation and entrepreneurship.
CO5: Analyze research and development projects.

Page 163

39












PSIT3P1: Project Documentation and
Viva
M. Sc (Information Technology) Semester – III
Course Name: Project Documentation and Viva Course Code: PSIT3P1
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- --

The learners are expected to develop a project beyond the undergraduate level. Normal web sites, web applications,
mobile apps are not expected. Preferably, the project should be from the elective chosen by the learner at the post
graduate level. In semester three. The learner is supposed to prepare the synopsis and documentation. The same
project has to be implemented in Semester IV.
More details about the project is given is Appendix 1.



PSIT302c :Cloud Application
Development
M. Sc (Information Technology) Semester – III
Course Name: Cloud Application Development Course Code: PSIT302c
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Course Objectives:
 To develop and deploy Microservices for cloud
 To understand Kubernetes and deploy applications on Azure Kubernetes Service
 To understand DevOps for Azure
 To follow the DevOps practices for software development
 To build APIs for Azure an d AWS

Page 164

40 Unit Details Lectures Outcomes
I Implementing Microservices: Client to microservices
communication, Interservice communication, data considerations,
security, monitoring, microservices hosting platform options.
Azure Service Fabric: Introduction, core concepts, supported
programming models, service fabric clusters, develop and deploy
applications of service fabric.
Monitoring Azure Service Fabric Clusters: Azure application,
resource manager template, Adding Application Monitoring to a
Stateless Se rvice Using Application Insights, Cluster monitoring,
Infrastructure monitoring. 12 CO1
II Azure Kubernetes Service (AKS): Introduction to kubernetes
and AKS, AKS development tools, Deploy applications on AKS.
Monitoring AKS: Monitoring, Azure monitor and analytics,
monitoring AKS clusters, native kubernetesdashboard,
Prometheus and Grafana.
Securing Microservices: Authentication in microservices,
Implenting security using API gateway pattern, Creating
application using Ocrlot and securing APIs with Azure AD.
Database Design for Microservices: Data stores, monolithic
approach, Microservices approach, harnessing cloud computing,
dataase options on MS Azure, overcoming application
development challenges.
Building Microservices on Azure Stack: Azure stack, Offering
IaaS, PaaS on -premises simplified, SaaS on Azure stack. 12 CO2
III .NET DevOps for Azure: DevOps introduction, Problem and
solution.
Professional Grade DevOps Environment: The state of
DevOps,professional grade DevOps vision, DevOps architecture,
tools for professional DevOps environment, DevOps centered
application.
Tracking work: Process template, Types of work items,
Customizing the process, Working with the process.
Tracking code: Number of repositories, Git reposit ory, structure,
branching pattern, Azure repos configuration, Git and Azure. 12 CO3
IV Building the code: Structure of build, using builds with .NET
core and Azure pipelines,
Validating the code: Strategy for defect detection, Implementing
defect detection.
Release candidate creation: Designing release candidate
architecture, Azure artifacts workflow for release candidates,
Deploying the release: Designing deployment pipeline,
Implementing deployment in Azure pipelines.
Operating and monitoring r elease: Principles, Architectures for
observability, Jumpstarting observability. 12 CO4
V Introduction to APIs: Introduction, API economy, APIs in
public sector.
API Strategy and Architecture: API Strategy, API value chain,
API architecture, API managemen t.
API Development: Considerations,Standards, kick -start API
development, team orientation.
API Gateways: API Gateways in public cloud, Azure API
management, AWS API gateway.
API Security: Request -based security, Authentication and
authorization. 12 CO5

Page 165

41
Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Building Microservices
Applications on Microsoft
Azure - Designing, Developing,
Deploying, and Monitoring Harsh Chawla
Hemant Kathuria Apress -- 2019
2. .NET DevOps for Azure
A Developer’s Guide to
DevOps Architecture the Right
Way Jeffrey Palermo Apress -- 2019
3. Practical API Architecture and
Development with Azure and
AWS - Design and
Implementation of APIs for the
Cloud Thurupathan
Vijayakumar Apress -- 2018
M. Sc (Information Technology) Semester – III
Course Name: Cloud Application Development Practical Course Code: PSIT3P2c
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- --


List of Practical:
10 practical covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.


Course Outcomes:
After completion of the course, a student should be able to:

CO01: Develop the Microservices for cloud and deploy them on Microsoft Azure.

CO02: Build and deploy services to Azure Kubernetes service.

CO03: Understand and build the DevOps way.

CO04: Thoroughly build the applications in the DevOps way.

CO05: Build the APIs for Microsoft Azure and AWS.

PSIT303c : Cloud Management
M. Sc (Information Technology) Semester – III
Course Name: Cloud Management Course Code: PSIT303c
Periods per week (1 Period is 60 minutes) 4
Credits 4
Hours Marks
Evaluation System Theory Examination 2½ 60
Internal -- 40

Page 166

42 Course Objectives:

 To Understand the Fundamental Ideas Behind Cloud Computing, The Evolution Of The Paradigm, Its
Applicability; Benefits, As Well As Current And Future Challenges;
 The Basic ideas And Principles In Data Center Design; Cloud Management Techniques And Cloud
Software Deployment Considerations;
 Different CPU, Memory And I/O Virtualization Techniques That Serve In Offering Software, Computation
 And Storage Services On The Cloud; Software Defined Networks (SDN) And Software Defined Storage
(SDS);
 Cloud Storage Technologies And Relevant Distributed File Systems, Nosql Databases And Object Storage;
 The Variety Of Programming Models And Develop Working Experience In Several Of Them.

Unit Details Lectures Outcome
I What is VMM? What's new in VMM
Get Started Release notes - VMM
Turn telemetry data on/off Deploy a VMM cloud Create a
VMM cloud Manage a VMM cloud Deploy a guarded host
fabric
Deploy guarded hosts Configure fallback HGS settings
Deploy a shielded VHDX and VM template Deploy a shielded
VM
Deploy a shielded Linux VM Deploy and manage a software
defined network (SDN) infrastructure Deploy an SDN
network controller Deploy an SDN SLB Deploy an SDN RAS
gateway Deploy SDN u sing PowerShell Set up a VM network
in SDN
Encrypt VM networks in SDN Allow and block VM traffic
with SDN port ACLs Control SDN virtual network bandwidth
with QoS Load balance network traffic Set up NAT for traffic
forwarding in an SDN Route traffic across networks in the
SDN infrastructure
Configure SDN guest clusters Update the NC server certificate
Set up SDN SLB VIPs Back up and restore the SDN
infrastructure
Remove an SDN from VMM Manage SDN resources in the
VMM fabric Deploy and manage Storage Spaces Direct Set up
a hyper -converged Storage Spaces Direct cluster Set up a
disaggregated Storage Spaces Direct cluster Manage Storage
Spaces Direct clusters Assign storage QoS policies for
Clusters How To Plan System requirements – VMM Plan
VMM installation P lan a VMM high availability deployment
Identify VMM ports and protocols Plan the VMM compute
fabric Plan the VMM networking fabric Identify supported
storage arrays Upgrade and install
Upgrade VMM Install VMM Install the VMM console Enable
enhanced console session Deploy VMM for high availability
Deploy a highly available VMM management server Deploy a
highly available SQL Server database for VMM Deploy a
highly available VMM library Set up TLS 1.2 Deploy update
rollups Back up and restore VMM Manage the VM M library
Library overview Add file -based resources to the VMM
library
Add profiles to the VMM library Add VM templates to the
VMM library Add service templates to the VMM library
Manage VMM library resources Manage virtualization servers 12 CO1

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43 Manage VMM host g roups Add existing Hyper -V hosts and
clusters to the fabric Add a Nano server as a Hyper -V host or
cluster Run a script on host
Create a cluster from standalone Hyper -V hosts Provision a
Hyper -V host or cluster from bare -metal Create a guest Hyper -
V cluste r from a service template Set up networking for
Hyper -V hosts and clusters Set up storage for Hyper -V hosts
and clusters Manage MPIO for Hyper -V hosts and clusters
Manage Hyper -V extended port ACLs Manage Hyper -V
clusters Update Hyper -V hosts and clusters Run a rolling
upgrade of Hyper -V clusters Service Hyper -V hosts for
maintenance Manage VMware servers Manage management
servers Manage infrastructure servers Manage update servers
Manage networking Network fabric overview Set up logical
networks Set up log ical networks in UR1 Set up VM networks
Set up IP address pools Add a network gateway Set up port
profiles Set up logical switches Set up MAC address pools
Integrate NLB with service templates Set up an IPAM server
Manage storage Set up storage fabric Set up storage
classifications Add storage devices Allocate storage to host
groups Set up a Microsoft iSCSI Target Server Set up a
Virtual Fibre Channel Set up file storage Set up Storage
Replica in VMM




II Service Manager What's new in Service Manager Get started
Evaluation and activation of Service Manager Service
Manager components Supported configurations System
requirements - Service Manager Release notes - Service
Manager Enable service log on Manage telemetry settings
How to Plan
Planning for Service Manager Plan for deployment Service
Manager editions Recommended deployment topologies
Operations Manager considerations Service Manager
databases
Port assignments Prepare for deployment Service Manager
performance Plan for performance and sca lability Plan for
hardware performance Deploy Deploy Service Manager
Deployment scenarios Install on a single computer Install on
two computers
Install on four computers Set up remote SQL Server Reporting
Services Use SQL Server AlwaysOn availability group s for
failover
Create and deploy server images Install on VMs Configure
PowerShell Register with the data warehouse to enable
reporting Deploy additional management servers Deployment
considerations with a disjointed namespace Learn about the
new Self Serv ice portal
Deploy the Self -Service portal Set up load balancing
Back up the encryption key Index non -English knowledge
articles
Troubleshoot deployment issues Deploy from a command line
Move databases Upgrade Upgrade Service Manager Upgrade 12 CO2

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44 the self -service portal to Service Manager 2016 Upgrade SQL
Server Reporting Services Set up a lab environment for
upgrade Prepare the production environment Prepare the lab
environment Run an upgrade Complete tasks after upgrade
Troubleshoot upgrade issues
Administer Use management packs to add functionality Use
connectors to import data Import data from Active Directory
Domain Services Import data and alerts from Operations
Manager
Import data from Configuration Manager Import run books
from Orchestrator Import data from VMM Use a CSV file to
import data
Optionally disable ECL logging for faster connector
synchronization Configuration items Configure incident
management Configure service level management Configure
workflows Configur e change and activity management
Configure release management Configure Desired
Configuration Management to generate incidents Configure
notifications Use the service catalog to offer services Use
groups, queues, and lists in Service Manager
Use runbooks t o automate procedures User interface
customization
Manage user roles Manage Run As accounts Manage
knowledge articles Configure and use Service Manager
cmdlets Manage the data warehouse Register source systems
to the data warehouse
Troubleshoot computer pr oblems with tasks Configure your
preference for sharing diagnostic and usage data Operate
Search for information Manage incidents and problems
Manage changes and activities Manage service requests
Manage release records
Data warehouse reporting and analyti cs Use and manage
standard reports
III What is Configuration Manager? Microsoft Endpoint
Configuration Manager FAQ What happened to SCCM?
Introduction
Find help for Configuration Manager How to use the docs
How to use the console Accessibility feat ures Software Center
user guide Fundamentals Configuration Manager
fundamentals
Sites and hierarchies About upgrade, update, and install
Manage devices Client management Security Role -based
administration Configuration Manager and Windows as a
Service
Plan and design Get ready for Configuration Manager
Product changes Features and capabilities Security and
privacy for Configuration Manager Security and privacy
overview
Plan for security Security best practices and privacy
information
Privacy statement - Configuration Manager Cmdlet Library
Additional privacy information Configure security
Cryptographic controls technical reference Enable TLS About
enabling TLS Enable TLS on clients Enable TLS on site
servers and remote site systems Common issues when 12 CO3

Page 169

45 enabli ng TLS 1Migrate data between hierarchies Migration
overview Plan for migration Planning for migration
Prerequisites for migration Checklists for migration
Determine whether to migrate data Planning the source
hierarchy
Planning migration jobs Planning clie nt migration Planning
for content deployment Planning to migrate objects Planning
to monitor migration Planning to complete migration
Configure source hierarchies and source sites Operations for
migrating Security and privacy for migration Deploy servers
and roles Deploy servers and roles Install infrastructure Get
installation media Before you run setup Setup reference Setup
downloader Prerequisite checker
Prerequisite checks Installing sites Prepare to install sites
overview
Prepare to install sites Prere quisites for installing sites Use the
setup wizard Use a command -line Command -line overview
Command -line options Install consoles Upgrade an evaluation
install
Upgrade to Configuration Manager Scenarios to streamline
your installation Configure sites and h ierarchies Configure
sites and hierarchies overview Add site system roles Add site
system roles overview Install site system roles Install cloud -
based distribution points About the service connection point
Configuration options for site system roles Databa se replicas
for management points Site components Publish site data
Manage content and content infrastructure Content
infrastructure overview Install and configure distribution
points Deploy and manage content Monitor content
Microsoft Connected Cache Trou bleshoot Microsoft
Connected Cache Run discovery Discovery methods overview
About discovery methods Select discovery methods Configure
discovery methods Site boundaries and boundary groups Site
boundaries and boundary groups overview Boundaries
Boundary gr oups Procedures for boundary groups High
availability High availability options Site server high
availability Flowchart - Passive site server setup Flowchart -
Promote site server (planned) Flowchart - Promote site server
(unplanned) Prepare to use SQL Ser ver Always On Configure
SQL Server Always On Use a SQL Server cluster
Custom locations for database files Configure role -based
administration
IV What's new in Orchestrator Automate with runbooks Get
started
Install Orchestrator Work with runbooks in the Orchestrator
console
Example runbook: Creating a runbook to monitor a folder
Release notes – Orchestrator Turn on/off telemetry How To
Plan
Database sizing and performance Feature performance
considerations System requirements – Orchestrator Design a
runbook Deploy Upgrade Orchestrator Deploy runbooks
Configure Orchestrator database connections Migrate
Orchestrator between environments Change the Orchestrator
database Manage Runbooks 12 CO4

Page 170

46 Design and build runbooks Create and test a sample runbook
Control runbook activities Monitor activities Runbook
properties
Track runbooks Install TLS Install and enable TLS 1.2
Manage Orchestrator Servers Runbook permissions Back up
Orchestrator
Bench mark Optimize performance of .Net activities
Configure runbook throttl ing Recover a database Recover web
components
Add an integration pack View Orchestrator data with
PowerPivot Change Orchestrator user groups Common
activity properties Computer groups Standard Activities
Orchestrator standard activities Alphabetical list o f Standard
Activities Ports and protocols of Standard Activities System
Run Program Run .NET Script End Process Start/Stop Service
Restart System Save Event Log Query WMI Run SSH
Command Get SNMP Variable
Monitor SNMP Trap Send SNMP Trap Set SNMP Variable
Scheduling Monitor Date/Time Check Schedule Monitoring
Monitor Event Log Monitor Service Get Service Status
Monitor Process Get Process Status Monitor Computer/IP Get
Computer/IP Status Monitor Disk Space Get Disk Space
Status Monitor Internet Application Get Internet Application
Status Monitor WMI File Management Compress File Copy
File Create Folder Decompress File Delete File Delete Folder
Get File Status Monitor File Monitor Folder Move File Move
Folder PGP Decrypt File PGP Encrypt File
Print File Renam e File Email Send Email Notification Send
Event Log Message Send Syslog Message Send Platform
Event Utilities Apply XSLT Query XML Map Published Data
Compare Values
Write Web Pages Read Text Log Write to Database Query
Database
Monitor Counter Get Counter Value Modify Counter Invoke
Web Services Format Date/Time Generate Random Text Map
Network Path Disconnect Network Path Get Dial -up Status
Connect/Disconnect Dial -up Text File Management Append
Line
Delete Line Find Text Get Lines Insert Line Read Line Sea rch
and Replace Text Runbook Control Invoke Runbook Initialize
Data Junction Return Data Orchestrator Integration Toolkit
Overview of Orchestrator Integration Toolkit Installation
Command Line Activity Wizard Integration Pack Wizard
Integration Packs Activ e Directory Active Directory activities
Add Computer To Group
Add Group To Group Add User To Group Create Computer
Create Group Create User Delete Computer Delete Group
Delete User Disable Computer Disable User Enable Computer
Enable User
Get Computer Get Group Get Organizational Unit Get User
Move Computer Move Group Move User Remove Computer
From Group
Remove Group From Group Remove User From Group
Rename Group Rename User Reset User Password Unlock
User Update Computer Update Group Update User

Page 171

47 V Data Protection Manager How does DPM work?
What can DPM back up? DPM -compatible tape libraries
Get Started DPM build versions DPM release notes
What's new in DPM What DPM supports How To
Plan Your DPM Environment Get ready to deploy DPM
servers
Prepare you r environment for DPM Prepare data storage
Identify compatible tape libraries Identify data sources you
want to protect Install or Upgrade DPM Install DPM
Upgrade your DPM installation Add Modern Backup storage
Deduplicate DPM storage Deploy DPM Deploy the DPM
protection agent Deploy protection groups Configure firewall
settings Offline backup Using own disk Protect Workloads
Back up Hyper -V virtual machines Back up Exchange with
DPM Back up SharePoint with DPM Back up SQL Server
with DPM Back up client com puters with DPM Back up file
data with DPM Back up system state and bare metal Back up
and restore VMware servers Back up and restore VMM
servers
Prepare to back up a generic data source Prepare machines in
workgroups and untrusted domains for backup Back up the
DPM server Monitor and Manage Monitor DPM Set up DPM
logging Generate DPM reports Use SCOM to manage and
monitor DPM servers Improve replication performance Use
central console to manage DPM servers 12 CO5

Books and References:
Sr. No. Title Author/s Publisher Edition Year
1. Microsoft SCVMM 2019 Whitepaper Microsoft 2019
2. Microsoft Endpoint Manager
2019 Whitepaper Microsoft 2019
3. Microsoft SCO 2019 Whitepaper Microsoft 2019
4. Microsoft SCOM 2019 Whitepaper Microsoft 2019
5. Microsoft SCSM 2019 Whitepaper Microsoft 2019
6. Microsoft DPM 2019 Whitepaper Microsoft 2019
7. Introducing Microsoft
System Center 2012 Mitch Tulloch with
Symon Perriman and
the System Center
Team Microsoft
Press 2012



M. Sc (Information Technology) Semester – III
Course Name: Cloud Management Practical Course Code: PSIT3P3c
Periods per week (1 Period is 60 minutes) 4
Credits 2
Hours Marks
Evaluation System Practical Examination 2 50
Internal -- -


List of Practical:
10 practicals covering the entire syllabus must be performed. The detailed list of practical
will be circulated later in the official workshop.