S. No

Course Code

Course Title

Course Content

CT – 601

Advanced Database and Hypothetical  Databases

Advanced concepts in Databases, Distributed and Object Oriented Databases, Hypothetical relations, Differential File, Hypothetical databases.

The Architecture and Design issues as discussed along with Performances, Security, Risk-Management and Fault recovery methods.

Applications in Business and Industries.

CT – 602

Decision Support System and Expert System

Decision Support System, Modeling and Analysis, Business Intelligence, Decision Support System Development, Knowledge management, Expert System, Knowledge Acquisition, Representation and Reasoning, Advanced Intelligent Systems, Intelligent Systems over the Internet.

CT – 603

Marketing /Methods of Business Research

Marketing in a changing World: Creating customer value and satisfaction, Strategic Planning and the Marketing process, The Marketing Environment, Marketing Research and Information Systems, Consumer Markets and Consumer Buyer Behavior, Promotion Products: Marketing Communication Strategy, Promoting Products: Personal Selling and Sales Management, Building Customer Relationships through Satisfaction, Value and Quality, Competitor Analysis and Competitive Marketing Strategies. Marketing research process, Defining the problem and determining the research objective, Research design, Secondary data sources, Standardized information sources, Observation, focus groups and other qualitative marketing methods.

CT – 604

Advanced Operations Research

The Nature of Operations Research, Formulating Problems and Objective Analysis, Types of Problems, Risk Situation, Sequential Decision Models, Linear Programming Model, Graphical Method, Sensitivity Analysis, Advanced Simplex Method, Big-M Method. Dynamic Programming, Queuing Theory, Network Analysis, CPM, PERT, Resources Allocation, Game Theory.

CT – 605

Fuzzy Logic

Classical Sets and Fuzzy Sets, Classical Relation and Fuzzy Relation, Properties of Membership Function, Fuzzification and Defuzzification, Logic and Fuzzy System, Development of Membership Function, Representation of Fuzzy Knowledge  in Relational Databases, Fuzzy Transactions, Fuzzy Association Rules in Relational Databases, Decision Making with Fuzzy Information, Fuzzy Classification, Fuzzy Classification Query Languages.

CT – 606

Strategic Marketing

Marketing strategies. Market-led strategic management, Strategic marketing planning, Portfolio analysis. Competitive marketing analysis. The Changing Market Environment, Industry analysis. Assessment of organizational resources, Customer analysis, Competitor analysis. Identifying current and future competitive marketing, Segmentation and positioning principles, Segmentation and positioning research, Selecting market targets. Creating Sustainable Competitive Advantage, Offensive and defensive competitive strategies, Competing through strategic alliances and networks, Competing through superior service and customer relationships, Competing through innovation and new product development, Competing through e-Marketing. Implementation through internal marketing.

CT – 607

Functional Genomics and Proteomics

UNIT I - GENOMICS

Prokaryotes and Eukaryotes, The structure, function and evolution of the human genome. Foundations of genomics. Strategies for large-scale sequencing projects, Genome library construction: YAC, BAC and PAC libraries of genome.

UNIT II- SEQUENCING AND MAPPING

Genome sequencing, Hierarchical and shot gun sequencing methods, Variation in sequencing methods, Pyrosequencing, Automation in genome sequencing, New generation sequencing methods, Mapping of genome: linkage mapping, High resolution physical mapping, Marker associated and clone assisted genome mapping.

UNIT III- SEQUENCE AND GENE EXPRESSION ANALYSIS

Sequence analysis, Databanks, data mining, Annotation of genome, Bioinformatics for the analysis of sequence data, approaches for determining gene expression patterns and functions, Functional genomics, Human disease genes Expression, Gene knockouts, gene expression profiling, microarrays, cDNA and Oligo array.

UNIT IV –

PROTEOMICS TOOLS

Tools for proteomics: 2D Electrophoresis, Liquid chromatography in proteomics, Protein identification – Mass spectrometry, peptide mass fingerprinting, protein sequencing, Structural proteomics- X-ray crystallography, NMR.

UNIT V - PROTEIN INTERACTIONS AND MICROARRAYS

Protein-Protein interactions, Library based methods, systematic complex analysis by Mass spectrometry, Protein interaction maps. Functional proteomics – protein chips, detection and quantification.

LIST OF EXPERIMENTS

1. Genome comparison

2. Genome rearrangements

3. Phylogenetic Reconstruction

4. Methods for detecting trans-membrane helices

5. Identification of proteins using database searches

6. Predicting Gene-Gene (Protein-Protein) interactions

CT – 608

Bioinformatics – Techniques and Applications

BIOLOGICAL DATABASES

Biological data types, Major biological databases and its classification, sequence and structure file formats, data mining.

SEQUENCE ANALYSIS

Methods of sequence alignment. Pair wise alignment- Global, local, dot plot and its applications. Words method of alignment- FASTA and its variations, BLAST- Filtered and gapped BLAST, PSIBLAST, Multiple sequence alignment- methods and Tools for MSA, Application of multiple alignments, Viewing and editing of MSA

MOLECULAR PHYLOGENY

Concepts of trees- Distance matrix methods, Character based methods. Solving UPGMA, NJ and small parsimony problems. Methods of evaluating phylogenetic methods- boot strapping, jackknifing

MACROMOLECULAR STRUCTURE ANALYSIS

Gene prediction, Conserved domain analysis, Protein visualization, Prediction of protein secondary structure, Tertiary structure prediction-Validation of the predicted structure using Ramachandran plot, Stereochemical properties.

COMPUTER AIDED DRUG DESIGNING

Protein Function Prediction, Metabolic Pathway analysis, Computer aided drug designing, Pharmacogenomics and Pharmacogenetics.

LIST OF EXPERIMENTS

1. Bioinformatics databases

2. Pairwise sequence alignment

3. Sequence similarity searching for sequences

4. Multiple sequence alignment and editing

5. Phylogenetic analysis using distance based methods & character based methods

6. Evaluation of trees

7. Gene prediction tools

8. Prediction Of secondary Structure of proteins

9. Sequence based prediction and Validation of 3d Protein structure

10. Docking studies

CT –  609

Advanced Natural Language Processing

Language and Text as Data

The Language Machine: ambiguity, applications

Corpus: text as data, tags, word tokens and types

Regular Expressions and Automata

Regular Expressions

Basic Regular Expression Patterns  Disjunction, Grouping, and Precedence .

FSA

Regular Language and FSA

Classifying Text by Machine Learning

Data Mining methodology: CRISP-DM

Text Classifiers

Word classes and POS Tagging

Grammar and Parsing: Syntactic Structures

Corpus Annotation and Evaluation

Semantics and Information Extraction

Representing Meaning

Semantic Analysis

Lexical Semantics

Named Entity Recognition

Rule-based Methods

Machine learning techniques

CT –  610

Statistical Image and Video Processing

Introduction: This course covers theory and applications of image and video processing based on statistical models for these signals and the imaging systems. Applications include image restoration, forensics, motion estimation for video, security, etc. Problems are solved by application of fundamental principles of statistical inference

Specific topics include:

Image modeling:  Model estimation and validation, autoregressive models, Markov random fields, multiresolution models, graphical models.

Spatial Processing: Classical and fundamental tools that help to deal with the noisy, blurry, and dark images.

Multidimensional Random Processes: Spectral representation, stationarity, isotropy, ergodicity.

Image Restoration: Image degradation models, Wiener filters, MAP estimators, regularized estimators, Bayesian estimators, set-theoretic estimators, learning, computational methods (EM algorithm, relaxation, alternating maximization methods.

Image Segmentation: How do we split an image or video in its core components

Video Processing

3-D and 2-D Motion models; ill-posedness of inverse problem; block matching; optical flow; transform-based models; motion-compensated prediction models; video restoration.

Geometric PDEs:

Partial differential equations and geometric deformations for problems like image enhancement and object detection.

Security Applications

Surveillance, authentication, encryption

CT  – 641    

Statistical Analysis  (replaced by CT-647)

Hypothesis testing

Null hypothesis

Common statistics tests such as z-test, t-test or F-test

Regression analysis

Correlation

Linear regression

Multivariate statistics

Covariance

Chi2 square test

Principal component analysis (PCA)

Correspondence analysis

CT –  642

Data Mining for Educational Data

    • Distance

    • Clustering

        ◦ K-means

        ◦ Hierarchical

        ◦ Mixed clustering and evaluation

    • Classification

        ◦ Entropy

        ◦ Decision trees

        ◦ Bayesian classification

        ◦ Evaluation

    • Association Rules

        ◦ Extraction with a priori algorithm

        ◦ Evaluation of extracted rules

    • Important applications in Education

CT  – 644

User Interface Design and Implementation

• Design

        ◦ Starting with human capabilities (including the human information processor model, perception, motor skills, color, attention, and errors) and using those capabilities to drive design techniques

        ◦ User-centered design

        ◦ Usability guidelines

        ◦ Interaction styles, and

        ◦ Graphic design principles

• Implementation

        ◦ Techniques for building user interfaces

        ◦ Wizard of Oz, and other prototyping tools

        ◦ Input/ output models

        ◦ Model-view-controller, layout, constraints, and toolkits

• Evaluation

        ◦ Techniques for evaluating and measuring interface usability

        ◦ User testing

CT  – 645

Advanced Databases

    • Issues in Database Integration

        ◦ Levels of Abstraction for Integration

        ◦ Semantic Integration

    • Distributed and Parallel Database

        ◦ Transparency of Distribution

        ◦ Database Integrity

        ◦ I/O Parallelism

        ◦ Intraquery Parallelism

    • Database Optimization

        ◦ Indexing

        ◦ Tables Combination

        ◦ Storage of Derived Data

CT  – 646

Research Methodologies

Introduction to Research

Nature, Scope and purpose

Developing the research question and establishing the framework

Research Process

Ethics and considerations

Types of Research

Types of Data and sources of data collection

Aims of research and its classification

Place & Time dimension in research

Research Methods

Scientific research designs

Experimental designs

Causal Comparative & Correlational Designs

Survey Research

Qualitative Research Designs

Historical Research

Methods and Tools

Instrumentation

Sampling

Validity & Reliability

CT  – 647    

Statistical Analysis  (Revised course for CT-641)

Statistical Inference & Hypothesis Testing

Confidence and significance level, Sample size determination, Point & interval estimates, Interval estimates and hypothesis for Population Mean, Population standard deviation, & Population proportion, Chi-square tests.

Regression and Correlation

Properties of least square, Simple linear regression, Non linear regression, Multiple regression, Statistical inference for regression, Choice of a Regression model, Correlation, Multiple and partial correlation, Coefficient of determination, Multicollinearity, Adequacy of the model.

Multivariate Statistics

Multivariate data and models, Multivariate Normal distribution, Principle Component Analysis, Factor Analysis, Canonical Correlation, Correspondence Analysis.

CT  – 651    

Automatic Speech Recognition

Acoustic theory of speech production, acoustic-phonetics, speech coding, signal representation, FFT, LPC, Mel Cepstrum, aspects of speech recognition systems, pattern classification, search algorithms, stochastic modelling, and language modeling techniques. Approaches to speech recognition, advanced techniques used for acoustic-phonetic modeling, effect of environment acoustic noise on speech signal, robust speech recognition, speaker adaptation, processing paralinguistic information, speech understanding, and multimodal processing.

CT  – 652

Machine Learning

Basic concepts of machine learning, supervised learning, logistic regression, perceptron, exponential family, generative learning algorithms, Gaussian discriminant analysis, Naive Bayes, Support vector machines, model selection and feature selection, Ensemble methods: Bagging, boosting. Evaluating and debugging learning algorithms, bias variance tradeoffs, worst case learning, clustering, K-means, PCA, ICA, reinforcement learning.

CT  – 653

Advanced Topics in Machine Learning

Fuzzy Logic Systems

Crisp and Fuzzy set, Basic set operations, Operations on Fuzzy Sets, Fuzzy relations, membership functions and uncertainty, Interrelation between features, Composition of fuzzy relations, Fuzzy additive model, Defuzzification by feature distance minimisation.

Genetic Algorithms & Evolutionary Computing

Structure of a genetic algorithm, schema theorem, building block hypothesis, encoding, fitness functions, fitness scaling. Genetic operators: crossover, mutation, reproduction, selection techniques. Mechanism of genetic algorithm, Applications of genetic algorithm.

Hidden Markov Model (HMM)

Discrete-Time Markov processes, First-Order Markov Models, First-Order Hidden Markov Models, Hidden Markov Model computation, Evaluation problem: Foward-Backward algorithm, Decoding problem: Viterbi algorithm, Learning problem: Baum-Welch algorithm, Implementation issues for HMMs.

CT  – 654

Statistics and Probability Theory

Descriptive Statistics and Sampling:  Measure of Central Tendency and Dispersion, Sampling Techniques, Sampling Distribution of Mean, Central Limit Theorem.

Probability Theory:  Set Theory, Applying Set Theory to Probability, Probability Axioms, Conditional Probability, Bayes Theorem, Markov Chain, Random variables and random process, Probability distribution.

Inferential Statistics:  Estimation and hypothesis testing, Test concerning mean, variance and proportion, Z-test, t-test, chi-square test, F-test, ANOVA, Design of Experiment.

Regression Analysis:  General Linear Model, Simple and Multiple Regressions, Non-linear Regression, Logistic Regression, Test concerning significance of Regression, ANOVA for Regression, Correlation and test of coefficient of correlation

Multivariate Analysis:  Principal Component Analysis, Factor Analysis, Discriminant Analysis, Cluster Analysis

CT  – 655

Linear Algebra and Approximation Theory

Matrices and Vector Spaces: Rank, Range, Null spaces, linear equations, vector spaces & subspaces, Bases & Dimensions, Subspaces connected with matrices.

Composition and Combination of Linear Transformation: Inverse linear transform, Kernel & Range, Range and null spaces, Change of basis and similarity. Linear transform and change of basis.

Diagonalisation: Dynamical systems, Eigen values and Eigen vectors, Diagonalisation of square matrix, Orthogonal Diagonalisation of symmetric matrices

Direct sum & Projections: Orthogonal Complements, Orthogonal projection, Minimising the distance to subspace, Fitting function to data: Least square approximation.

Complex matrices & vector spaces: Complex number, Complex plane, Complex solutions of differential and difference equations

Approximation Theory: Statistical Approximation theory (Application to Wavelet, Neural Network & Fuzzy logic), Pade Approximation, Orthogonal Polynomial, Logarithm potential theory.

CT  – 656

Image Processing

Overview of Image Processing Systems. Digital Image Representation. Image Quantization: Uniform and Non- Uniform, Visual Quantization (Dithering).

    • Image capture  (color, RGB, image irradiance, exposure, dynamic range, noise, sampling)

    • Image Contrast Enhancement: Linear and non-liner Stretching, Histogram Equalization.

    • Continuous and discrete-time

    • Fourier Transforms in 2D; and linear convolution in 2D.

    • Image smoothing and image sharpening by spatial domain linear filtering, Edge detection-I

    • Edge detection – II.

    • Discrete Fourier transform in ID and 2D, and image filtering in the DFT domain.

    • Median filtering and Morphological filtering.

    • Colour representation and display; true and pseudo colour image processing-I.

    • Colour Image Processing – II.

    • Image sampling and sampling rate conversion (resize).

    • Lossless Image Compression.

    • Loss Image Compression.

    •  Imaging Geometry.

    • Object Recognition.

    • Camera model  (homogeneous coordinates, extrinsic and intrinsic parameters)

    • Homographies structure from motion (egomotion, factorization)

    • Epipolar geometry, estimating the fundamental matrix

CT  – 657

Computer Vision

    • Video processing; image formation and acquisition, image processing, image understanding and representations.

    • Features of object of interest. Types of features, low level mid-level and high level features,

    • Features computation, such as size, color, SIFT and HoG descriptors features.

    • Pattern recognition,

    • Cameras and projection models.

    • Low-level image processing methods such as filtering and edge detection, mid-level vision topics such as segmentation and clustering, shape reconstruction from stereo, as well as high-level vision tasks such as object recognition, scene recognition, face detection and human motion categorization.

CT  – 658

Cloud Computing

    • Introduction to Cloud Computing, Building Blocks and Service Models in Cloud Computing.

    • Cloud Computing Architectural Framework - Cloud Benefits, Business scenarios, Cloud Computing Evolution, cloud vocabulary, Essential Characteristics of Cloud Computing, Cloud deployment models, Cloud Service Models, Multi- Tenancy, Approaches to create a barrier between the Tenants, cloud computing vendors.

    • Data Centers: Historical Perspective, Datacenter Components, and Design Considerations.

    • Cloud Resource Management: Resource Abstraction, Resource Sharing, Sandboxing, Case Studies: Google Apps, Google App Engine and Amazon EC2.

    • Cloud Storage Introduction to Storage Systems: Cloud Storage Concepts, Distributed File Systems, Cloud Databases, Case Study: Amazon Storage

    • Cloud Security: Current state of the art in cloud security, Security policies to mitigate security risks in the cloud, Private cloud,

Open research questions in cloud security

CT  – 659

Advanced Topics in Applied Cryptography

Introduction and classification of different cryptographic algorithms and protocols

Privacy-Enhancing Technologies

    • Privacy-Preserving Data Collection and Data Publishing

    • Privacy-Preserving Data Mining

    • K-Anonymity

    • Anonymous communications

    • Anonymous credentials

    • Group signatures

    • Privacy and anonymity in peer-to-peer architectures

    • Privacy-enhanced access control or authentication/certification

Advanced Crypto Algorithms and Protocols

    • Zero-knowledge proof

    • Oblivious Transfer

    • Secure Multiparty Computation

    • Digital Cash

    • Secret Sharing

    • Threshold Cryptography

    • Identity-Based Encryption

    • Attribute-Based Encryption

Emerging applications of applied cryptography.

CT  – 660

Privacy Protection

Introduction and Fundamentals

    • History of privacy protection; the legal framework; privacy in everyone’s daily life

    • Anonymity, unlinkability, unobservability

    • Economics of privacy; targeted advertisement and ad blocking; why privacy is often not protected

    • Privacy by Design; data minimization

    • Identity management and anonymous credentials

Data Privacy

    • k-anonymity, l-diversity, t-proximity

    • Privacy in databases; private information retrieval; differential privacy

    • Privacy in cloud computing

    • Privacy and biometrics

    • Medical data; the case of genomic privacy

Privacy in the Internet and in Mobile Networks

    • Anonymous routing and anonymous Web surfing

    • Privacy in peer-to-peer systems; Crowds

    • Privacy in online social networks

    • Privacy in cellular networks

    • Location privacy and its quantification

    • Reputation systems and privacy

Advanced Topics

    • Anonymous payment systems (Bitcoin, eCash)

    • Cross-domain attacks against privacy

CT –  661

Spatial Computing

    • Introduction to Spatial Computing

    • Spatial Query Languages

    • Spatial Networks, Conceptual and mathematical models

    • SQL extensions, CONNECT statement, RECURSIVE statement, Storage and data structures, Algorithms for connectivity query and shortest path

    • Spatial Data Mining, Spatial Pattern Families, Spatial data types and relationships, Location Prediction model, Hotspots, Spatial outliers, Co-locations and Co-occurrences

    • Volunteered Geographic Information (VGI)

    • Positioning: GPS, Wifi and Cellular Positioning, Content-based Positioning, Geoparsing, Location-field Positioning

    • Cartography: Overview of Maps and Mapping, Reference Maps, Thematic Maps, Spatialization

    • Current research trends and future research directions.

CT –  662

Advanced Software Architectures

    • Architectural view models, architectural styles and frameworks – Layered, Client-Server, 3-Tier, N-Tier, Quality attributes, design guidelines.

    • Architectural Description Languages (ADLs) and Architectural Specification Languages (ASLs) – capturing architecture, skeletal system patterns.

    • Architectural design patterns – Distributed, Event-driven, Frame-based, Repository-centric.

    • Model Driven Architecture (MDA) – Platform independence, Interoperability, Metadata integration.

    • Service Oriented Architecture (SOA) – Service orientation, web services, logical components, Security: confidentiality, integrity.

    • Architectures in support of cloud computing – Infrastructure as a Service (IaaS),  Platform as a Service (PaaS),  Software as a Service (SaaS)

CT –  663

Advanced Software Testing

CT –  664

Quantum Computation and Information

    • Basic concepts: Postulates of Quantum Mechanics

    • Quantum bits – qubits, Dirac notation, Combining qubits using the tensor product

    • Measuring qubits, Performing operations on qubits

    • The Bloch Sphere representation

    • The quantum circuit model

    • Simple quantum protocols: teleportation,

superdense coding.

    • Quantum Algorithms: Deutsch’s algorithm, Deutsch-Jozsa Algorithm and the Bernstein-Vazirani Algorithm

    • Shor’s algorithm for factoring

    • Grover’s algorithm for searching

    • Entanglement and Bell’s theorem

    • Open quantum systems

    • Quantum error correction

    • Quantum cryptography

    • Emerging research topics from quantum computation and information

CT –  665

Advanced Topics in Wireless Networks

    • Wireless networking challenges

    • Wireless MAC protocols including the 802.11 family, Bluetooth and personal area networks, etc.

    • Wireless Mobile Ad Hoc Networks (introduction, Characteristics, Ad Hoc vs. Cellular Networks, Applications, Challenges, Routing Protocols

     (DSDV, AODV, DSR, TORA, OLSR, ZPR)

    • Wireless Sensor Networks (Introduction, Applications, Factors influencing Performance of WSN, Architecture and Communication Protocols, Challenges in WSNs)

    • Wireless Mesh Networks, Characteristics, WMN vs MANET, Architecture, Applications, Critical Factors Influencing Performance.

    • Vehicular Ad Hoc Networks (VANETs) Routing Protocols on VANETs, Multicast and Broadcast Protocols on VANETs, Reliable Communications on VANETs

    • Traffic and mobility modeling, Simulations of wireless networks

CT –  666

Quantum Key Distribution Protocols

    • Quantum tools and a first protocol

    • Encrypting quantum bits with the quantum one-time pad

    • Sharing a classical secret using quantum states

    • Verifying entanglement using a Bell experiment

    • The (min)-entropy including the smooth min-entropy

    • Uncertainty principles: simple version BB84

    • From imperfect information to (near) perfect security

    • Privacy amplification

    • Randomness extraction using two-universal hashing

    • Key distribution with limited Eve

    • The need for information reconciliation

    • Practical error correction in key distribution protocols

    • Quantum key distribution protocols

    • BB84 states and Six state protocol states

    • Purifying protocols using entanglement

    • Security from the tripartite uncertainty relation

    • Quantum cryptography in practice

CT – 667

Advanced Intrusion Detection Techniques

    • Overview of Intrusion Detection methods.

    • Malware Detection: Obfuscation, Polymorphism, Payload based detection of worms, Botnet detection/takedown

    • Techniques of intrusion detection: Signature-based detection techniques, Anomaly-based detection techniques, Specification-Based, Behavioral Techniques

    • Machine learning methods with application to intrusion detection.

    • Network Intrusion Detection: Signature-based solutions (Snort, etc.), Data-mining-based solutions (supervised and unsupervised), Deep packet inspection

    • Host Intrusion Detection: Analysis of shell command sequences, system call sequences, and audit trails, Masquerader/Impersonator/Insider threat detection.

    • Advanced persistent threats

CT – 668

Internet of Things Systems and Security

    • IoT systems architecture, hardware platforms.

    • Wireless technologies and networking protocols.

    • Security and privacy concepts in IoT.

    • Privacy issues in IoT, IoT Authentication and Authorisation, IoT Data Integrity.

    • Web Based Attacks and Implementation in IoT.

    • Denial of Service, Sniffing, Phishing, DNS Hijacking, Pharming, Defacement etc.

    • Attack Surface in IoT and Threat Assessment

    • IoT Protocol Inbuilt Security Features

    • IoT Industry Applications: Smart Home, Smart Agriculture, Smart Retail Supply, Smart Healthcare, Smart Grid, Smart Cities

CT – 669

Semantic Web Technologies

CT – 670

Information Retrieval and mining Massive datasets

Information retrieval and boolean search; Vector space retrieval model; Term weighting and vector space model; Indexing; Term Document Index Matrices for massive datasets; Term Frequency- Inverse Document Frequency (TF-IDF) Vectors; Search engine evaluation metrics for massive datasets; Link analysis; Search engine optimization; Text classification and clustering; Supervised learning and Deep learning methods for information retrieval.

37

CT –671

Distributed Ledger Technology

    • Introduction to distributed ledger, Transactions and Digital Signatures.

    • The consensus layer. Basic Properties. Proof of Work.

    • Robust Transaction Ledgers. Properties and Objectives.

    • Permissioned, permissionless ledgers.

    • Privacy Issues. Anonymity, Pseudonymity, Unlinkability. Zero-Knowledge Proofs.

    • Scalability Issues. Byzantine agreement protocols.

    • Blockchain as a platform. Smart Contracts.

    • Secure multiparty computation techniques and their application to blockchain protocols.

    • Alternative techniques to proof of work for blockchain protocols, proof of stake/space.

    • Game theoretic analysis of blockchain protocols.

Name and object registries. Reputation systems. Policy issues related to blockchain.

38

CT –672

Advance Topics in Network Security

    • Modern Spyware/Malware techniques and protection methods.

    • Advance Persistent Threat (APT)

    • DNS security, Phishing and Email spamming, IP prefix hijacking

    • Denial of service; distributed denial of service; botnets;

    • Methods to detect and defend complex denial of service attacks.

    • Intrusion detection and prevention techniques; honey pots.

    • Firewalls, Distributed Firewalls

    • Security Information and Event Management (SIEM).

    • Security in Mobile Ad hoc Networks.

    • Designs for privacy preserving networks, like Tor, Mixnets, I2P, or DC nets.

    • Security and Privacy issues in Internet of Things (IoT).

    • Emerging topics in Network Security.

39

CT –673

Software Requirement Analysis & Specifications

40

CT –674

Specification and Software Measurements and Metrics

41

CT –675

Advanced Learning Analytics

    • Theory and Learning Analytics

    • Ethical and Learning Analytics

    • Predictive Modelling in Teaching and Learning

    • Natural Language Processing and Learning Analytics

    • Emotional Learning Analytics

    • Multimodal Learning Analytics

    • Learning Analytics Dashboards

    • Learning Analytics Implementation Design

    • Provision of Data-Driven Student Feedback in LA and EDM

    • Applying Recommender Systems for Learning Analytics Data

42

CT –676

Cyber Intelligence

The cyber intelligence lifecycle from planning to dissemination.

Cyber intelligence methodologies.

Closed-source, open-source and multi-source intelligence.

Threat actor attribution and analysis.

The diamond model.

Cyber defense kill-chain.

Threat hunting.

Techniques and procedures for information sharing.

Cyber threat intelligence sharing

Cyber situation awareness (cyber intelligence products).

A comparative analysis of important cyber intelligence frameworks.

43

CT-677

Cloud Forensics

Cloud forensics dimensions, Cloud forensics challenges. Evidence collection and preservation in cloud, Virtual machines and hypervisor forensics, Cloud storage forensics, Cloud network forensics, SDN & NFV forensics, Applications forensics in cloud, Privacy preserving forensics in cloud computing, Container forensics and incident response, Legal issues associated with the gathering of evidence, chain of custody and presentation of finding in cloud computing, Digital forensics and data confidentiality, Anti-forensics techniques in cloud computing, Digital forensics as a cloud service. Digital forensics readiness for cloud services.

44

CT-678*

Advanced Cloud Security

Cloud computing top security challenges, Cloud data security, Cloud security best practices, Cloud network security, Microservices architecture pattern, application containers and microservices security. Secure design and development in cloud computing, Incident response in cloud computing, identity and access management in cloud computing, Best practices in implementing a secure microservices architecture, Resilience in cloud computing, Secure serverless architecture, Cloud security services management architecture, Hybrid cloud security. Multi-cloud security, Privacy preserving cloud security solutions, Security as a service (SaaS)

45

CT-679

Special Topics in Generative Artificial Intelligence (AI)

Generative Adversarial Networks (GANs), Overview of different GAN architectures, Few-shot and zero-shot learnings models, Training GANs using a simple dataset (such as MNIST), Applications and Real-world use cases of GANs, Machine learning applications, Natural language processing, Deep Learning and its applications. Super-resolution techniques, Introduction to Super Resolution Single-image super-resolution to fill in missing details of the images Generative models for super-resolution, Explainability and Interpretability of learnt models, Understanding adversarial attacks Ethical considerations, Applications of Generative Models Text generation through Transformer-based models (e.g., GPT, BERT)