Advanced Malware and Network Anomaly Detection

Shape-shifting malware, APTs, and zero-day exploits increasingly evade traditional detection methods, creating critical security gaps that AI/ML are uniquely positioned to address.

Building on the classification fundamentals from his Introduction to AI for Cybersecurity course, Dr. Lanier Watkins now guides you through creating advanced detection systems that identify threats signature-based methods consistently miss. Using Jupyter notebooks and ML libraries like scikit-learn, you’ll develop sophisticated security solutions for malware analysis, botnet detection, and industrial control system protection.

By course completion, you’ll possess a toolkit of AI-powered security implementations—including models for self-modifying malware detection, pattern recognition for unknown threats, and transparent AI for network anomaly detection. These skills enable you to implement adaptive security solutions that don’t rely solely on known signatures, significantly strengthening your defense capabilities.