Course Modules:
Module 1: Introduction to AI in Cybersecurity
Overview of modern cybersecurity challenges
Role of AI and machine learning in cyber defense
Types of cyber threats: malware, phishing, APTs, zero-days
Module 2: Data Sources in Cybersecurity
Network traffic data, log files, system events
Structured vs. unstructured security data
Feature engineering from cybersecurity datasets
Module 3: Machine Learning for Threat Detection
Classification models for malware and intrusion detection
Supervised vs. unsupervised approaches
Use of algorithms like SVM, Random Forests, and XGBoost
Module 4: Anomaly Detection Systems
Identifying abnormal behavior in network activity
Autoencoders, Isolation Forests, and clustering for anomaly detection
Reducing false positives with AI
Module 5: Deep Learning for Cybersecurity
Applying LSTM and CNNs to detect sequential and visual patterns
Behavioral-based malware detection
Threat hunting with deep neural networks
Module 6: Intrusion Detection and Prevention Systems (IDPS)
Traditional IDS vs. AI-enhanced IDS
Real-time monitoring and response
Open-source tools and frameworks (Snort, Zeek + ML)
Module 7: NLP in Cyber Threat Intelligence
Analyzing threat reports and dark web data
Text classification for phishing and social engineering
Extracting Indicators of Compromise (IoCs) using NLP
Module 8: AI for Access Control & Identity Protection
Biometric authentication with machine learning
Behavioral biometrics (keystroke, mouse dynamics)
Detecting credential stuffing and insider threats
Module 9: Securing Cloud and IoT with AI
AI for cloud workload protection
Threat detection in IoT networks
Edge AI security for connected devices
Module 10: Capstone Project
Choose from:
Building an ML-powered intrusion detection system
Designing an AI bot to monitor phishing emails
Creating a real-time anomaly dashboard for a simulated network
Deliver demo, report, and risk analysis
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