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M. Tech Information and Cyber Security- CBCS S. No Subject Code M. Tech Information and Cyber Security- CBCS Subject S. No CBCS R-16 L T P C Code Foundation Course 1. 2160MA103 Probability and Statistics FC 3 2 0 4 Program Core 2161IT108 Computer Oriented Optimization 2. PC 4 0 0 4 Techniques 3. 2161IT109 Modern Number Theory PC 4 0 0 4 4. 2161IT110 Foundations of Modern Networking PC 4 0 0 4 2161IT111 Hacking Techniques and Incidents 5. PC 4 0 0 4 Response 6. 2161IT112 Cloud Computing PC 4 0 0 4 7. 2161IT113 Modern Cryptography PC 4 0 0 4 8. 2161IT114 Principles of Information Security PC 3 0 0 3 9. 2161IT115 Cyber Security Principles PC 3 0 0 3 Total Credits 30 Specific Elective 10. 2162IT121 Principles of Cyber Physical Systems PE 3 0 0 3 11. 2162IT122 Intrusion Detection Systems PE 3 0 0 3 12. 2162IT123 Cyber Crimes and Laws PE 3 0 0 3 13. 2162IT124 Cyber Forensics PE 3 0 0 3 2162IT125 Bitcoin and Cryptocurrency 14. PE 3 0 0 3 Technologies Performance Evaluation of Computer 15. 2162IT126 PE 3 0 0 3 Systems 16. 2162IT127 Bio-Informatics PE 3 0 0 3 Independent Learning 17. 2163MG401 Research Methodology Independent Learning 2 18. 2163IT402 ICT Tools Independent Learning 2 19. 2163IT501 Research Seminar Independent Learning 20. 2163IT502 Field Study 2 (Anyone) 21. 2163IT801 Internship 22. 2163GE401 Business Communication Independent Learning 2 Project Work 23. 2164IT601 Project Phase 1 Project Work 10 24. 2164IT701 Project Phase 2 Project Work 16 L – Lecture; T – Tutorial; P – Practical; C – Credit COURSE CODE COURSE TITLE L T P C 2160MA102 PROBABILITY AND STATISTICS 3 2 0 4 Course Category: Foundation Course A. Preamble : To provide an in-depth knowledge about probability, sampling, Correlation and Random process B. Course Outcomes : Upon the successful completion of the course, students will be able to: Level of learning CO domain (Based on Course Outcomes Nos. revised Bloom’s taxonomy) CO1 Understanding basic concepts of probability K2 CO2 Applying basic concepts of different types of sampling K3 Applying basic concepts of Hypothesis testing and Chi-square CO3 K3 testing to solve simple problems CO4 Applying basic concepts of Correlation K3 Applying basic concepts of Random process to solve simple CO5 K3 problems C. Correlation of COs with POs : COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 CO1 H L CO2 H H H CO3 H H M L CO4 H L M M CO5 H L H H M H- High; M-Medium; L-Low D. Course Content : UNIT I (9) Axioms of probability - Conditional probability - Total probability - Baye’s theorem - Random variable - Probability mass function - Probability density function – Properties – Moments - Moment generating function and their properties - Binomial, Poisson, Geometric, Negative binomial, Uniform, Exponential, Gamma and Weibull distribution and their properties. UNIT II (9) Sampling: different types of sampling – Sampling distribution – Sampling distribution of Mean Point Estimation of parameters: general concepts of Estimation – Unbiased estimators – Variance of a point Estimator – Standard error – Method of point estimation (method of moments – method of maximum likelihood) – Statistical intervals for a single sample: confidence interval on the mean of a normal distribution with variance known – Confidence interval on the mean of a normal distribution with variance unknown – Confidence interval on the variance and standard deviation of a normal distribution. UNIT III- TESTING OF HYPOTHESIS (9) Hypothesis testing: one sample and two sample tests for means and properties of large samples (z-test), one sample and two sample tests for means of small samples (t-test), F-test for two sample standard deviations – Chi-square test for single sample standard deviation – Chi-square tests for independence of attributes and goodness of fit. UNIT IV CORRELATION AND REGRESSION (9) Correlation – Scatter diagram – Karlpearson coefficient of correlation – calculation of the correlation coefficient for a bivariant frequency distribution – rank correlation – repeated rank – Regression – lines of regression – regression curves – regression coefficients – multiple and partial correlation – coefficient of partial correlation – generalization – multiple correlation. UNIT V RANDOM PROCESSES (9) Classification – Stationary process – Markov Process – Poisson process – Discrete parameter – Markov chain – Chapman Kolmogorov equations – Limiting distributions. Total: 45 +15(Tutorial) = 60 Periods Text Books: 1. Richard A. Johnson and C. B. Gupta, Probability and Statistics for Engineers, (7th Edn.), Pearson Education, Indian Impression – 2006 COURSE CODE COURSE TITLE L T P C COMPUTER-ORIENTED OPTIMIZATION 2161IT108 4 0 0 4 TECHNIQUES Course Category: Program Core A. Preamble : Optimization Techniques is one of the most advanced fields of computer science which involves use of Mathematics, Statistics, Management, Information Technology and Information Sciences in discovering new information and knowledge from large databases and optimize Human effort overall in Decision making process. B. Course Outcomes : Upon the successful completion of the course, students will be able to: Level of learning CO domain (Based on Course Outcomes Nos. revised Bloom’s taxonomy) Optimization problems defining, understanding and K2 CO1 classification. Formulating Linear Programming problem and similar such K3 CO2 problems into appropriate forms and problem solving. CO3 Working with Non Linear Programming problems K3 CO4 More on Non Linear Programming problems and Game Theory. K3 CO5 Queueing Theory a overview and problem solving. K3 C. Correlation of COs with POs : COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 CO1 H L CO2 H H H CO3 H H M L CO4 H L M M CO5 H L H H M H- High; M-Medium; L-Low D. Course Content : UNIT I (9) Statement of Optimization Problem, Classification of Optimization Problems, Classical Optimization Techniques- Single Variable Optimization, Multivariable Optimization with no Constraints, with Equality Constraints, and Inequality Constraints, Convex Programming Problem. UNIT II (9) Formulation of Linear Programming, Integer Programming, 0-1 Programming and Mixed Integer Programming Problems. Solving by Computer using Graphical Method, Simplex Method, Revised Simplex Method, Transportaion Algorithm ,Karmarkar’s Method and Hungarian Method. UNIT III (9) Fundamental(2/2) - Congruence Theory , Primitive Roots and Elliptic Curve Formulation of One Dimesional Nonlinear Programming Problems related to Computer Science and Engineering; Solving by Computer Using Fibonacci Elimination Method, Quadratic Interpolation Method, and Newton Method.Formulation of Nonlinear Unconstrained Optimization Problems related to Computer Science and Engineering; Solving by Computer Using Hook-Jeeves Method and Steepest Decent (Cauchy) Methods UNIT IV PRIMALITY TESTING AND INTEGER FACTORIZATION (9) Formulation of Nonlinear Constrained Optimization, Geometric Programming, Quadratic Programming, Dynamic Programming and Stochastic Programming Problems related to Computer Science and Engineering. Game Theory – Two-person Zero-sum Game and Mixed Stategy Games; Applications of Game Theory in Computer Science and Engineering UNIT V DISCRETE LOGARITHMS (9) Basic Structure of Queueing Models, The Birth-and-Death Process, Queueing Models based on Birth- and-Death Process, Queueing Models Involing Non-exponetiual Distributiuons, A Priority-Discipline Queueing Models, Queueing Networks and Queueing Decision Models. Applications of Queueing Models in Computer Science and Engineering. Total: 45 Periods Text Books: 1. Singiresu S Rao, Engineering Optimization : Theory and Practice Third Enlarged Edition, New Age International Publishers, 2003 2. Hamdy A Taha, Operations Research- An Introduction, Ninth Edition, Pearson, 2013 3. Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research, Sevetrh Edition, 2002 References: 1. Articles published in reputed journals / transactions related to Computer Science and Engineering COURSE CODE COURSE TITLE L T P C 2161IT109 MODERN NUMBER THEORY 4 0 0 4 Course Category: Program Core A. Preamble : To provide an in-depth knowledge about number theory, computation theory and computational number theory in modern cryptography B. Course Outcomes : Upon the successful completion of the course, students will be able to: Level of learning CO domain (Based on Course Outcomes Nos. revised Bloom’s taxonomy) Understanding basic concepts of number theory, computation CO1 K2 theory and computational number theory in modern cryptography Applying basic concepts of elementary number theory to solve CO2 K3 simple problems Applying basic concepts of congruence theory, primitive roots CO3 K3 and elliptic curves to solve simple problems. Applying basic concepts of primality testing to solve simple CO4 K3 problems Applying basic concepts of integer factorization to solve simple CO5 K3 problems Applying basic concepts of discrete logarithms to solve simple CO6 K3 problems C. Correlation of COs with POs : COs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 CO1 H L CO2 H H CO3 H M L L CO4 H M M L CO5 H L H H M H- High; M-Medium; L-Low D. Course Content : UNIT I INTRODUCTION TO CRYPTOGRAPHY (9) Introduction - Number Theory, Computation Theory, Computational Number Theory and Modern Cryptography. Secret-Key Cryptography - Cryptography and Cryptanalysis, Classic Secret-Key and Modern Secret-Key Cryptography. UNIT II FUNDAMENTALS OF NUMBER THEORY (9) Fundamental(1/2) - Basic Algebraic Structures, Divisibility Theory and Arithmetic Functions. UNIT III CONGRUENCE THEORM (9) Fundamental(2/2) - Congruence Theory
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