Introduction
AI has become an essential tool in cybersecurity, capable of detecting threats, analyzing anomalies, and automating incident response. This course introduces the concepts and techniques of AI-driven cybersecurity. Participants will explore how ML models identify patterns of malicious behavior and how generative models can simulate attacks. Hands-on activities provide experience building detection models. By the end, learners will understand both the potential and the limitations of AI in cybersecurity.
Course Objectives
- Learn how AI enhances cybersecurity operations
- Explore ML models for threat detection
- Understand anomaly detection and predictive analytics
- Study adversarial AI attacks
- Build simple cybersecurity ML tools
Target Audience
- Cybersecurity professionals
- ML engineers
- IT administrators
- Students in security or AI
- SOC and incident response personnel
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: AI & Cybersecurity Foundations• Threat landscape
• AI applications in security
• Common attack types
• Data sources: logs, flows, events
• Hands-on: Parse security data0 - Day 2: Threat Detection Models• Classification models
• Feature extraction from logs
• Malware detection basics
• Evasion risks
• Hands-on: Build a detection model0 - Day 3: Anomaly Detection• Unsupervised learning
• Statistical anomaly detection
• ML approaches
• Behavior profiling
• Hands-on: Detect anomalies0 - Day 4: Adversarial AI & Defensive Measures• Adversarial attacks
• Poisoning and evasion
• Defensive ML techniques
• Red-team simulations
• Hands-on: Simple adversarial example0 - Day 5: AI-Enabled Security Operations• Automated incident response
• SOC augmentation
• Predictive threat analytics
• Ethical and legal considerations
• Capstone project0
Instructor
Lessons:
