AI-Powered Cybersecurity

Macquarie University via Coursera Specialization

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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.

Syllabus

Course 1: Machine Learning for Cyber Threat & Anomaly Detection
Course 2: Adversarial AI: Attacking, Defending & Governing ML Systems
Course 3: Cyber Incident Response: Triage, Containment & Recovery

Courses

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As AI becomes central to cybersecurity defence, attackers are increasingly targeting the AI systems themselves. Model poisoning, adversarial inputs, backdoor exploits, and model stealing are active threats — and most security teams are unprepared to detect or defend against them. This course gives you the knowledge and practical strategies to secure ML systems from the inside out. You'll examine how ML systems are manipulated through adversarial inputs, poisoning attacks, and threat models across real-world use cases including malware detection and fraud analytics. You'll then explore advanced attack vectors: model poisoning, information leakage, model stealing, and backdoor exploits, and assess their impact on data privacy, intellectual property, and user safety. From attack to defence, you'll learn to apply secure algorithm design, differential privacy, and guardrail protection — and conduct AI security testing using red, purple, and blue teaming approaches. The course closes with AI governance: responsible AI principles, bias mitigation, transparency, data ethics, and the global regulatory frameworks governing AI in cybersecurity. Designed for security analysts, ML engineers, security architects, and risk and compliance professionals working with AI-powered security systems. Job skills taught: Adversarial AI Defence · AI Security Testing · ML Threat Modelling · Model Robustness · Differential Privacy · Red/Blue/Purple Teaming · AI Governance · Responsible AI · Regulatory Compliance for AI Features Coursera Coach, Dialogues and Role Plays - a smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
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When a cyber attack hits, the speed and structure of your response determines everything — how much damage is done, how quickly systems recover, and whether your organisation emerges stronger. Yet structured incident response remains one of the most underdeveloped capabilities in security teams worldwide. This course gives you a complete, operational incident response skillset — from the first alert through to post-incident learning. You'll begin with preparation: assessing your security landscape, establishing a Computer Security Incident Response Team (CSIRT), and developing crisis communication strategies for staff, leadership, stakeholders, and media. You'll then develop triage and analysis skills — distinguishing real incidents from noise, identifying early indicators of compromise, and analysing logs and alerts to assess the scale and impact of a breach. Moving from analysis to action, you'll apply containment strategies that isolate compromised systems while maintaining business continuity, and eradicate threats including malware and insider attacks. The final stage covers recovery, post-incident documentation, root cause analysis, and presenting lessons learned to executive audiences. Interactive role plays simulate real-world pressure: CSIRT activation, SOC manager briefings, live breach response, and leadership debriefs. Job skills taught: Incident Detection & Classification · CSIRT Management · Incident Triage & Analysis · Threat Containment · System Eradication & Recovery · Post-Incident Documentation · Post-Incident Review · Crisis Communication · SOC Operations · Security Resilience Features Coursera Coach, Dialogues and Role Plays - a smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.
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Machine learning is transforming how organisations detect cyber threats — but most security professionals lack hands-on experience building and deploying ML models. This course closes that gap, taking you from core ML concepts to practical, applied threat detection on real cybersecurity datasets. You'll start with the foundations: model training, learning types, and measuring model accuracy. You'll also learn how attackers exploit ML systems through inference, poisoning, and adversarial input — giving you a security-first perspective from the start. From there, you'll move into hands-on application. You'll load, preprocess, train, and test classification and regression models to identify malware, detect fraud, and analyse network traffic. You'll apply artificial neural networks to classify malware binaries and behavioural patterns. In the final section, you'll build network anomaly detection models using K-Nearest Neighbors (KNN) and One-Class SVM to identify outlier traffic and distinguish normal behaviour from potential attacks. Designed for security analysts, SOC teams, IT engineers, and data scientists entering cybersecurity. Basic cybersecurity knowledge is recommended. Job skills taught: Machine Learning for Cybersecurity · Threat Detection · Malware Analysis · Network Anomaly Detection · ML Model Training and Evaluation · Classification and Regression Modelling · Fraud Detection · Artificial Neural Networks · Network Traffic Analysis Features Coursera Coach, Dialogues and Role Plays - a smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course.