Machine Learning for Cyber Threat & Anomaly Detection

Overview

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.

Syllabus

• Introduction to AI and Machine Learning in Cybersecurity
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 Threat Detection and 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.
• Mini Project
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.

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Taught by

Matt Bushby

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Subjects

Computer Science