Machine Learning is a powerful tool combating cyber threats. This module moves beyond theory to hands-on ML techniques for cyber defense. You will identify malware, detect network traffic anomalies, and find fraud. Learn to load, preprocess, train, and test classification and regression models using practical tools. Algorithms help automate threat detection and accelerate response. By the end, you will run ML models on cyber datasets, gaining new insight and readiness.

Modern cyber attacks often travel through the digital veins of an organisations, its networks. This module shows how Machine Learning identifies unusual patterns and detects hidden threats. You will study malware foundations, from binaries to behavioral types, and how ML models analyze network traffic to flag anomalies. Through practical exercises, you will work with malware datasets and apply machine learning algorithms, including artificial neural networks, to classify malicious behavior. Gain skills to create intelligent defense mechanisms that learn from evolving threats, enhancing cyber resilience.

Cyber attackers mimic normal traffic. This module teaches how machine learning transforms anomaly detection, helping you spot compromise signals. You will study foundational techniques like K-Nearest Neighbors (KNN) and One-Class Support Vector Machines (SVM), applying them to network logs to detect outliers and distinguish traffic. Through hands-on experimentation, gain experience building models that automatically identify abnormal network behaviors. By the end, you will use machine learning for advanced threat detection, making defenses smarter and more adaptive.

In this module, you will build and evaluate an ML model to detect anomalous network traffic and classify malicious binaries. The project allows you to build a comprehensive portfolio artefacts demonstrating your end-to-end capabilities.