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Course Detail

Course Name AI for Cyber Security Applications
Course Code 26CY731
Program M. Tech. in Cyber Security
Credits 3

Syllabus

Foundations of AI for Cyber Security: Introduction to Artificial Intelligence, Evolution of AI, AI vs. Machine Learning vs. Deep Learning vs. Generative AI, Types of Learning – Supervised, Unsupervised, Semi-supervised and Reinforcement Learning, Neural Networks and Decision Trees. Cybersecurity Foundations: CIA Triad, Threat Actors and Attack Vectors. Data Collection and Preprocessing, Feature Engineering, Feature Selection, Dimensionality Reduction, Model Training and Validation, Overfitting and Underfitting, Performance Evaluation Metrics. Python for Security Analytics, Security Datasets, Log Collection and Preprocessing Pipelines.

Machine Learning and Deep Learning for Threat Detection: Classification, Clustering and Dimensionality Reduction Techniques, Perceptron, Multi-Layer Perceptron, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Variational Autoencoders, Generative Adversarial Networks (GANs), Large Language Models (Architectural Overview), AI-based Anomaly Detection, Applications of Machine Learning and Deep Learning in Cyber Security.

AI-Driven Cyber Security Operations: AI System Architectures and Security Applications, Foundation Models, Retrieval-Augmented Generation (RAG), Agentic AI, AI for Threat Detection and Prevention, Malware Analysis and Classification, Vulnerability Assessment and Prioritization, AI-assisted Penetration Testing and Red Teaming, Phishing and Spam Detection, Deepfake Detection and Media Forensics, Natural Language Processing for Threat Intelligence, Security Information and Event Management (SIEM), AI-powered Security Operations Centres (SOC), Threat Hunting, Real-time Threat Detection, Explainable AI (XAI), Continuous Authentication, AI-assisted Incident Response and Forensics, AI-based Cyber Risk Assessment, Secure AI Lifecycle, MLOps and SecOps Integration, Threat Modelling for AI Systems, AI Risk Management, Adversarial Machine Learning, LLM and Prompt Security Risks, Emerging Trends and Applications in AI-driven Cyber Security.

Course Outcome

Prerequisites: Statistics and Probability

Course Outcome(CO) Bloom’s Taxonomy Level
CO1 Explain the concepts of Artificial Intelligence, Machine Learning, Deep Learning, Foundation Models, and their applications in Cyber Security L2
CO2 Apply Machine Learning and Deep Learning techniques to develop intelligent solutions for cyber security analytics, anomaly detection, and threat detection. L3
CO3 Analyze and implement AI-driven cyber security solutions using modern AI architectures, intelligent security operations, secure AI practices, and AI risk management. L4

CO-PO Mapping (3-High, 2-Medium, 1-Low)

CO/PO PO 1 PO 2 PO 3 PO 4 PO 5 PO 6 PO 7 PO 8 PO 9 PO 10 PSO1 PSO2 PSO3
CO 1 2 2 1 1 2 2 3 1
CO 2 3 2 2 2 3 1 1 2 3 2
CO 3 3 3 3 3 3 2 1 2 2 3 3 3

Textbooks/References

  1. Corrado Aaron Visaggio, Fabio Di Troia, Francesco Mercaldo, Mark Stamp, Artificial Intelligence for Cybersecurity, July 15, 2022, Springer International Publishing.
  2. T. Dunning and E. Friedman, Practical Machine Learning – A New Look at Anomaly Detection, O’Reilly, 1st edition, 2014.
  3. Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press, 2016.
  4. Alessandro Parisi, Hands-On Artificial Intelligence for Cybersecurity, Packt Publishing, August 2, 2019, ISBN: 9781789805178, 1789805171
  5. Kim-Kwang Raymond Choo, Leslie F. Sikos, Data Science in Cybersecurity and Cyberthreat Intelligence, Springer International Publishing, February 5, 2020, ISBN: 9783030387884, 3030387887

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