Syllabus — Intelligent Networking

Year I, Part II — MSNCS, IOE Pulchowk, Tribhuvan University. 4 credits.

Chapter 1 — Foundations of Intelligent Networking (8 marks)

• 1.1 Evolution of Intelligent Networking
• 1.2 Overview of Latest Networking Technologies: SDN, IPv6, IBN, DCN, CDN
• 1.3 Overview of Quantum and Self-Organisation Networks
• 1.4 Fundamentals of Cognitive Networking and Its Applications
• 1.5 Basics of Machine Learning and AI in Networking
• 1.6 Supervised, Unsupervised, and Reinforcement Learning in Networking

Chapter 2 — Intelligence in SDN (12 marks)

• 2.1 Overview of SDN Architecture
• 2.2 Advantages of Integrating Intelligence in SDN
• 2.3 Adaptive QoS and QoE Management in SDN
• 2.4 Deep Learning Applications in SDN for Traffic Prediction and Classification
• 2.5 Intelligent NFV and Virtual Network Function (VNF) Placement
• 2.6 Design and Architecture of AI-Powered SDN Controllers
• 2.7 Data-Driven Decision Making in SDN Environments
• 2.8 Integration of SDN with Cloud and Edge Computing Architectures
• 2.9 AI-Driven Network Security in SDN
• 2.10 Anomaly Detection and Behavioural Analysis in SDN Environments

Chapter 3 — Data Centric Networking (10 marks)

• 3.1 Overview and Concepts of DCN
• 3.2 Host-Centric vs Data-Centric Networking
• 3.3 Architectural Concepts of Named Data Networking (NDN)
• 3.4 Benefits and Challenges of DCN
• 3.5 Applications of AI in DCN
• 3.6 AI-Based Cache Placement and Replacement Techniques
• 3.7 AI-Based Content Naming and Discovery Techniques
• 3.8 Dynamic Content Caching Using Reinforcement Learning
• 3.9 AI-Driven Name-Based Routing Protocols
• 3.10 Secure Content Distribution and Access Control with AI

Chapter 4 — Intent-Based Networking (IBN) (10 marks)

• 4.1 Overview of IBN Concepts and Architecture
• 4.2 Traditional Networking vs IBN
• 4.3 Benefits and Challenges of IBN
• 4.4 Machine Learning for Intent Recognition and Translation
• 4.5 Role of NLP in Intent Translation and Parsing
• 4.6 Understanding and Processing User-Defined Intents
• 4.7 Automated Threat Detection and Mitigation in IBN
• 4.8 Integration of SDN and NFV with Intelligent IBN
• 4.9 Concept of Network Slicing in 5G/6G with IBN

Chapter 5 — Quantum Networking (10 marks)

• 5.1 Evolution from Classical to Quantum Networking
• 5.2 Classical vs Quantum Networks
• 5.3 Applications and Benefits of Quantum Networking
• 5.4 Overview of Quantum Bits (Qubits), Superposition and Entanglement
• 5.5 Quantum Communication Protocols: QKD – BB84, Entanglement-Based QKD
• 5.6 Quantum Teleportation Protocols
• 5.7 Quantum Link / Network / Transport Layer Protocols
• 5.8 Quantum Repeater Chains and Entanglement Distribution
• 5.9 Security Threats and Vulnerabilities in Quantum Networks
• 5.10 Quantum AI for Network Traffic Analysis and Anomaly Detection

Chapter 6 — Future Directions and Research Challenges in Network Intelligence (10 marks)

• 6.1 Intelligent Network Monitoring, Automation and Management
• 6.2 Self-Configuring, Self-Optimising and Self-Healing Networks
• 6.3 Resource Optimisation and Scaling with ML Algorithms
• 6.4 AI-Based Load Balancing Algorithms in SDN
• 6.5 Dynamic Traffic Engineering and Load Balancing Using AI
• 6.6 Quantum Networking with SDN, NDN and IBN
• 6.7 Blockchain for Secure and Decentralised Networking
• 6.8 Emerging Trends and Open Research Challenges in Latest Networking