This course explores how Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of cybersecurity. Participants will learn to apply AI techniques for threat detection, anomaly detection, malware analysis, fraud prevention, and incident response. The course blends theory with hands-on labs, preparing participants to design smarter, more adaptive cybersecurity systems.

Course Agenda:

Introduction to AI and Cybersecurity

• Evolution of cybersecurity threats

• Role of AI/ML in modern cybersecurity

• Overview of common applications: IDS, malware detection, risk scoring

Machine Learning Essentials for Cybersecurity

• Supervised vs unsupervised learning

• Data preparation: feature engineering for cybersecurity datasets

• Introduction to classification, clustering, and anomaly detection

AI for Threat Detection and Response

• Building a basic intrusion detection system (IDS)

• Using clustering for unknown threat detection

• Case study: AI-based threat hunting tools

Malware and Phishing Detection

• Machine learning for malware classification

• NLP for phishing email detection

• Hands-on: Build a phishing classifier using logistic regression/SVM

Anomaly Detection Techniques

• Statistical anomaly detection

• Isolation Forest, Autoencoders, and One-Class SVMs for cybersecurity

• Hands-on: Detect anomalies in network traffic logs

Adversarial Machine Learning

• Introduction to adversarial attacks

• Evasion, poisoning, and model extraction threats

• Strategies for building robust ML models in cybersecurity

Ethical and Legal Considerations

• Ethical concerns around AI and surveillance

• Privacy, transparency, and fairness in AI systems

• Regulatory frameworks (GDPR, AI Act)

Hands-On Exercises