Cyber attacks are growing more sophisticated, and machine learning is now central to how organisations detect and respond to them. But attackers are increasingly targeting AI systems themselves — and most security professionals are not prepared. This Specialization gives you a rare combination of skills: applying ML to detect threats, hardening AI systems against adversarial attacks, and executing structured incident response with operational confidence. You'll build and train ML models on real cybersecurity datasets, classify malware using artificial neural networks, and detect network anomalies using KNN and One-Class SVM. You'll analyse how ML systems are attacked through poisoning, adversarial inputs, and model stealing — and learn to defend using differential privacy and red, purple, and blue teaming. You'll also develop operational skills to prepare, detect, triage, contain, eradicate, and recover from cyber incidents, including CSIRT management, crisis communication, and executive reporting. Designed for security analysts, SOC teams, IT engineers, data scientists entering cybersecurity, and security architects. Basic cybersecurity knowledge is recommended.
What you'll learn
apply machine learning to detect cyber threats
build and train ML models on cybersecurity datasets
classify malware using artificial neural networks
detect network anomalies with KNN and One-Class SVM
understand adversarial attacks on ML systems
defend against model stealing and input poisoning
manage incident response effectively
Course objectives
equip security professionals with the ability to use ML in cybersecurity
prepare analysts to defend AI systems against attacks
develop operational skills for incident management