B.Tech Syllabus (7th Sem Information Technology)

Seventh semester syllabus for Information Technology at Bihar Engineering University

7th Semester Courses

Paper Code	Paper Title	L	Credits
1057xx	Cryptography & Network Security	3	3
1057xx	Data Mining & Warehousing	3	3
1057xx	Artificial Intelligence	3	3

Subject Details

Cryptography & Network Security

Module 1: Introduction to Cryptography

Security goals, services and mechanisms. Attack types. Classical encryption techniques: Symmetric cipher model, Substitution techniques, Transposition techniques.

Module 2: Symmetric Key Cryptography

Block ciphers, Data Encryption Standard (DES), Advanced Encryption Standard (AES), Cipher block modes of operation.

Module 3: Asymmetric Key Cryptography

Public-key cryptography principles, RSA algorithm, Diffie-Hellman key exchange, Elliptic Curve Cryptography (ECC).

Module 4: Message Authentication & Hash Functions

Authentication requirements, Message Authentication Code (MAC), Hash functions, Secure Hash Algorithm (SHA), Digital signatures.

Module 5: Network Security

Authentication applications (Kerberos), Email security (PGP, S/MIME), IP Security (IPSec), Web security (SSL/TLS).

Module 6: System Security

Intruders, Malicious software, Viruses, Firewalls, Security standards.

Data Mining & Warehousing

Module 1: Data Warehousing

Introduction, Multidimensional data model, OLAP operations, Data warehouse architecture, Data warehouse implementation.

Module 2: Data Preprocessing

Data cleaning, Data integration, Data reduction, Data transformation and discretization.

Module 3: Data Mining Primitives

Data mining functionalities, Classification of data mining systems, Data mining task primitives, Integration of data mining system with data warehouse.

Module 4: Association Rule Mining

Frequent itemset mining methods: Apriori algorithm, FP-growth algorithm. Mining various kinds of association rules.

Module 5: Classification & Prediction

Decision tree induction, Bayesian classification, Rule-based classification, Prediction: Linear regression, Non-linear regression.

Module 6: Cluster Analysis

Types of data in cluster analysis, Partitioning methods: k-means, k-medoids. Hierarchical methods, Density-based methods.

Artificial Intelligence

Module 1: Introduction

Definition of AI, history, applications. Intelligent agents: environment, rational behavior, types of agents.

Module 2: Problem Solving & Search

State space search, uninformed search (BFS, DFS, Depth-limited), informed search (A* search, Heuristics), Constraint Satisfaction Problems (CSP).

Module 3: Knowledge Representation & Logic

Propositional logic, First-order logic, Inference in FOL, Unification, Resolution. Ontological engineering, Categories and objects.

Module 4: Uncertain Knowledge & Reasoning

Probability theory, Bayes' rule, Bayesian networks, exact and approximate inference. Temporal models, Hidden Markov Models (HMM).

Module 5: Machine Learning

Supervised learning: Decision trees, Neural networks, Support Vector Machines (SVM). Unsupervised learning: Clustering. Reinforcement learning.

Module 6: Natural Language Processing & Robotics

Language models, Information extraction, Machine translation. Robotics: perception, planning, and acting.