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Syllabus — Generative AI and Security

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

Chapter 1 — Introduction to Generative AI (10 marks)​

  • 1.1 Generative AI Overview and Importance in Technology
  • 1.2 Historical Evolution of Machine Learning to Generative Models
  • 1.3 Discriminative vs Generative Models
  • 1.4 Deep Learning Basics — Neural Networks, Backpropagation
  • 1.5 Probabilistic Foundations — Bayesian Learning, Likelihood Estimation
  • 1.6 Popular GenAI Models — GANs, VAEs
  • 1.7 Transformers — Self-Attention, Architecture, GPT Dominance
  • 1.8 GenAI Model Developments — GPT, BERT, DALL-E, Diffusion, Hybrid Models
  • 1.9 Interplay of AI and Cybersecurity — Benefits and Risks

Chapter 2 — Generative AI for Reconnaissance and Digital Footprinting (8 marks)​

  • 2.1 AI-powered Reconnaissance — Identifying Targets Using AI-driven OSINT Tools
  • 2.2 Digital Footprinting Automation with Generative Models
  • 2.3 Preventive Measures — Reducing Attack Surfaces and Secure Configurations

Chapter 3 — Penetration Testing and Vulnerability Analysis with AI (8 marks)​

  • 3.1 Generative AI in Automated Exploit Development and Testing
  • 3.2 Vulnerability Scanning and AI-driven Prioritization
  • 3.3 AI Tools for Enhancing Penetration Testing Workflows
  • 3.4 Defensive Strategies — Patching, Monitoring, System Hardening

Chapter 4 — Threat Intelligence and Anomaly Detection (8 marks)​

  • 4.1 Using AI for Threat Intelligence Gathering and Analysis
  • 4.2 Generative Models for Adversarial Behavior Prediction
  • 4.3 Detecting Anomalies Using Machine Learning Techniques
  • 4.4 Countering Generative AI-based Attacks with Real-time Monitoring

Chapter 5 — Social Engineering and Phishing Attacks (8 marks)​

  • 5.1 Generative AI for Phishing Content Generation and Spear-phishing
  • 5.2 Crafting Social Engineering Strategies with AI
  • 5.3 Defensive Measures — Awareness Training, Email Filtering, Sandboxing

Chapter 6 — Malware Development and Wireless Attacks (8 marks)​

  • 6.1 AI in Malware Creation — Obfuscation, Polymorphic Malware, Evasion
  • 6.2 Generative AI in Wireless Attacks — WPA Cracking, Spoofing
  • 6.3 Defense Strategies — Secure Wi-Fi Configurations, IDS, Endpoint Protection

Chapter 7 — Advanced Security Operations and AI Risk Management (10 marks)​

  • 7.1 Security Operations and Monitoring with AI — SIEM and SOC Workflows
  • 7.2 Building Resilient AI Systems — Adversarial Robustness, Secure Coding
  • 7.3 AI in Incident Response and Forensics
  • 7.4 Frameworks for Managing AI Risks — NIST AI RMF, ISO 27001

Chapter 8 — Emerging Trends and Ethical Considerations (8 marks)​

  • 8.1 Future Attack Scenarios — Autonomous AI-driven Cyberattacks
  • 8.2 Ethical Concerns — Dual-use AI and Societal Risks
  • 8.3 Legal and Compliance Considerations for GenAI in Cybersecurity
  • 8.4 Preparing for Quantum AI and Post-Quantum Security Challenges
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