Overview
AI for Cyber Security | Defend Smarter, Not Harder In today’s high-stakes cyber landscape, artificial intelligence (AI) and machine learning (ML) are no longer futuristic add-ons—they are essential pillars of a modern cyber defence strategy. This course is your hands-on, practitioner-focused guide to understanding how AI and ML are being used to detect, disrupt, and defend against cyber threats in real time. 🔐 Smarter Threat Detection.
Stronger Defences. Real-World Readiness.
Built by Macquarie University’s Cyber Skills Academy—ranked in the top 1% of universities globally and recognised as Australia’s leading cyber security school—this course has been co-designed with industry to ensure practical, real-world impact. It brings together technical depth and tactical awareness, with a focus on applications that are relevant, actionable, and urgently needed by today’s organisations.
Key topics include:
• Build foundational knowledge of AI and ML concepts, tasks (classification/regression), accuracy trade-offs, and the unique risks they face in cyber contexts. • Apply ML tools and models to real-world security problems, including malware analysis, fraud detection, deep packet inspection, and network monitoring. • Analyse network traffic using anomaly detection techniques powered by supervised and unsupervised ML methods, such as k-nearest neighbours and one-class SVM. • Unpack malware behaviour and experiment with ML-driven analysis to identify malicious binaries, understand malware types, and apply artificial neural networks to detection tasks. Dive deep into adversarial machine learning, learning how attackers manipulate models with poisoning and evasion attacks—and how to defend against them by building more robust, resilient systems. ⚙️ Important Note:
While no prior AI/ML experience is required, some basic familiarity with Python programming is recommended to get the most out of the practical activities and hands-on labs. 🧠 Building Models That Fight Back This course is designed for cyber security professionals, SOC analysts, engineers, data scientists, and tech leaders looking to future-proof their security strategies with intelligent automation and machine-driven defence techniques.
Syllabus
- Artificial Intelligence (AI) and Machine Learning (ML) Concepts
Artificial Intelligence (AI) and Machine Learning (ML) are transforming the way we defend against cyber threats—offering the power to detect patterns, respond to anomalies, and adapt to evolving risks at machine speed. But with great capability comes complexity and new vulnerabilities. In this module, you’ll build a strong foundation in AI and ML, tailored specifically for cyber security applications. You’ll explore the core concepts behind machine learning—how models are trained, what types of learning exist, and how we measure their accuracy and effectiveness. But you’ll also look under the hood at the darker side of AI: the ways attackers can exploit ML systems through inference, poisoning, and adversarial input. By the end of this module, you'll not only understand how ML can support cyber defence, but also the new attack surfaces it introduces—and how to critically evaluate its strengths, weaknesses, and limitations in the real world.
- Application of Machine Learning in Cyber Security
Machine Learning isn’t just a buzzword—it’s a powerful tool already being used to combat some of the most pressing cyber threats facing businesses today. In this topic, you’ll go beyond theory and get hands-on with ML techniques that are shaping the future of cyber defence. From detecting malware to identifying anomalies in network traffic and uncovering fraud, you’ll explore how ML models are applied across real-world cyber security use cases. Learn how to load, view, and preprocess datasets, then train and test classification and regression models using practical tools and workflows. This is where AI gets real. You’ll see firsthand how algorithms can help automate threat detection, accelerate response, and augment human judgement in high-stakes environments. By the end of this module, you’ll be equipped to run your own ML models on cyber datasets—unlocking new levels of insight and readiness.
- Machine Learning for Network Traffic Analysis
Modern cyber attacks often travel through the digital veins of an organisation—its networks. In this topic, you'll uncover how Machine Learning can serve as a powerful diagnostic tool, capable of identifying unusual patterns and detecting threats hiding in plain sight. You’ll explore the foundations of malware—from binaries to behavioural types—and how ML models can analyse and interpret network traffic to flag anomalies in real-time. Through practical exercises, you'll work with malware datasets and apply machine learning algorithms, including artificial neural networks, to spot and classify malicious behaviour before it causes harm. By mastering these techniques, you’ll develop the skills to create intelligent defence mechanisms that continuously learn from evolving threats—pushing your cyber resilience far beyond static rule-based systems.
- Machine Learning for Network Anomaly Detection
Cyber attackers are constantly evolving, often slipping past traditional defences by mimicking normal traffic patterns. In this topic, you’ll learn how machine learning transforms anomaly detection—enabling you to spot the subtle signals of compromise before damage is done. You’ll explore foundational techniques such as K-Nearest Neighbours (KNN) and One-Class Support Vector Machines (SVM), applying them to real-world network logs to detect outliers and distinguish between legitimate and malicious traffic. Through hands-on experimentation, you’ll gain experience in building models that can automatically identify abnormal behaviours in networks—no predefined rules required. By the end of this module, you'll be equipped to use machine learning for advanced threat detection, making your organisation’s defences smarter, faster, and more adaptive.
- Attacks on Machine Learning and Defences
As machine learning becomes more embedded in cyber defences, so too do the methods for breaking it. In this module, you’ll step into the mind of the adversary to understand how machine learning systems can be manipulated, bypassed, and broken—and more importantly, how to defend against it. You’ll explore adversarial machine learning through real-world examples of threat models, adversarial inputs, and poisoning attacks. You’ll learn how seemingly harmless data can be weaponised to compromise models, and how attackers exploit vulnerabilities during both the training and inference phases. But it's not all about offence. This module also dives into the defensive playbook—equipping you with practical techniques to build more resilient models and implement countermeasures that can withstand these emerging threats. Whether you're deploying ML in malware detection, intrusion systems, or fraud analytics, this module will help you safeguard your models and preserve their trustworthiness in the face of sophisticated attacks.
Taught by
Matt Bushby
Subjects
Computer Science
