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.
Applied Learning Project
The Specialization Capstone places you in the role of a cybersecurity analyst responding to a multi-stage attack on a fictitious enterprise organisation.
In Stage 1, you'll build and evaluate an ML model to detect anomalous network traffic and classify malicious binaries. In Stage 2, you'll analyse a simulated adversarial attack on a deployed ML model, identify the attack type, and recommend a defence strategy. In Stage 3, you'll lead a structured incident response from detection through containment and recovery, concluding with a post-incident review and executive briefing.
This project builds a portfolio-ready artefact demonstrating end-to-end capability across ML threat detection, adversarial AI defence, and operational incident response.
