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Machine Learning isn鈥檛 just a buzzword鈥攊t鈥檚 a powerful tool already being used to combat some of the most pressing cyber threats facing businesses today. In this topic, you鈥檒l 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鈥檒l 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鈥檒l 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鈥檒l be equipped to run your own ML models on cyber datasets鈥攗nlocking new levels of insight and readiness.

Modern cyber attacks often travel through the digital veins of an organisation鈥攊ts 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鈥檒l explore the foundations of malware鈥攆rom binaries to behavioural types鈥攁nd 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鈥檒l develop the skills to create intelligent defence mechanisms that continuously learn from evolving threats鈥攑ushing your cyber resilience far beyond static rule-based systems.

Cyber attackers are constantly evolving, often slipping past traditional defences by mimicking normal traffic patterns. In this topic, you鈥檒l learn how machine learning transforms anomaly detection鈥攅nabling you to spot the subtle signals of compromise before damage is done. You鈥檒l 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鈥檒l gain experience in building models that can automatically identify abnormal behaviours in networks鈥攏o predefined rules required. By the end of this module, you'll be equipped to use machine learning for advanced threat detection, making your organisation鈥檚 defences smarter, faster, and more adaptive.

As machine learning becomes more embedded in cyber defences, so too do the methods for breaking it. In this module, you鈥檒l step into the mind of the adversary to understand how machine learning systems can be manipulated, bypassed, and broken鈥攁nd more importantly, how to defend against it. You鈥檒l explore adversarial machine learning through real-world examples of threat models, adversarial inputs, and poisoning attacks. You鈥檒l 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鈥攅quipping 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.