Overview

AI for Cyber Security: Defend Smarter, Not Harder Artificial intelligence (AI) and machine learning (ML) are essential for modern cyber defense. This course provides a hands-on guide to understanding how AI and ML detect, disrupt, and defend against cyber threats. This program focuses on practical applications needed by organizations. Key topics include: • Build foundational AI and ML concepts, including model training, learning types, and accuracy. • Apply ML tools and models to security problems like malware analysis, fraud detection, and network monitoring. • Analyze network traffic using anomaly detection with supervised and unsupervised ML methods (e.g., k-nearest neighbors, one-class SVM). • Experiment with ML-driven analysis to identify malware and apply artificial neural networks for detection. • Understand adversarial machine learning, including poisoning and evasion attacks, and how to build resilient systems. Basic familiarity with Python programming is recommended for practical activities and labs. This course is designed for cyber security professionals, SOC analysts, engineers, data scientists, and tech leaders seeking to enhance security strategies with intelligent automation and machine-driven defense.

Syllabus

AI and Machine Learning Concepts

Artificial Intelligence (AI) and Machine Learning (ML) transform cyber defense by detecting patterns and responding to anomalies. This module builds a strong foundation in AI and ML for cyber security applications. You will study core machine learning concepts, including model training, learning types, and effectiveness measurement. You will also examine how attackers exploit ML systems through inference, poisoning, and adversarial input. By the end, you will understand ML's role in cyber defense, its new attack surfaces, and how to evaluate its strengths and limitations.

Machine Learning Applications in Cyber Security

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.

Machine Learning for Network Traffic Analysis

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.

Machine Learning for Network Anomaly Detection

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.

Attacks on Machine Learning and Defences

As machine learning integrates into cyber defenses, so do methods for breaking it. This module helps you understand how machine learning systems are manipulated and how to defend against it. You will examine adversarial machine learning through examples of threat models, adversarial inputs, and poisoning attacks. Learn how data can compromise models and how attackers exploit vulnerabilities. This module also covers defensive techniques to build resilient models and implement countermeasures. Safeguard your models in malware detection, intrusion systems, or fraud analytics against sophisticated attacks.