Do I need ML experience to take this course?

<text variant="body1">No prior machine learning experience is required. The course builds your ML foundation from the ground up, starting with core concepts before moving to hands-on model building. Basic cybersecurity knowledge is recommended so you can connect the technical content to real-world security scenarios.

What tools and techniques will I use in this course?

<text variant="body1">You'll work with classification and regression models, artificial neural networks, K-Nearest Neighbours (KNN), and One-Class Support Vector Machines (SVM), all applied to real cybersecurity datasets including malware samples and network logs.

How does this course fit into the AI-Powered Cybersecurity Specialization?

<text variant="body1">This is the first course in the Specialization. It builds the ML foundation you'll need for the second course — which covers how ML systems are attacked and defended — and gives you the threat detection context that underpins the operational incident response skills in the third course.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Machine Learning for Cyber Threat & Anomaly Detection

Machine Learning for Cyber Threat & Anomaly Detection

This course is part of AI-Powered Cybersecurity Specialization

Instructor: Matt Bushby

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Evaluate the role, strengths, and limitations of ML in cybersecurity, including its vulnerability to inference and poisoning attacks.
Build and train supervised classification and regression models on real-world cybersecurity datasets to detect malware and fraud.
Apply artificial neural networks to analyse malware binaries and classify malicious behavioural patterns using real datasets.
Construct network anomaly detection models using KNN and One-Class SVM to identify outlier traffic and detect attacks.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

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Build your subject-matter expertise

This course is part of the AI-Powered Cybersecurity Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 5 modules in this course

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.

Module details

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.

What's included

2 assignments13 plugins

2 assignmentsTotal 30 minutes

End of module practice quiz15 minutes
End of module quiz15 minutes

13 pluginsTotal 237 minutes

Overview5 minutes
Introduction10 minutes
An Industry Perspective15 minutes
Concepts and definitions of Machine Learning2 minutes
Learning tasks - classification and regression25 minutes
Accuracy of machine learning models25 minutes
Attacks on machine learning - an overview25 minutes
Inference attacks25 minutes
Adversarial input attacks25 minutes
Poisoning attacks25 minutes
Model stealing25 minutes
Summary15 minutes
References15 minutes

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.

What's included

2 assignments11 plugins

2 assignmentsTotal 30 minutes

End of module practice quiz15 minutes
End of module quiz15 minutes

11 pluginsTotal 149 minutes

Overview5 minutes
Introduction1 minute
Malware analysis10 minutes
Network anomaly detection20 minutes
Deep packet inspection5 minutes
Fraud detection5 minutes
Loading, viewing and preprocessing datasets40 minutes
Training and testing a classification model25 minutes
Training and testing a regression model25 minutes
Summary10 minutes
References3 minutes

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.

What's included

1 assignment6 plugins

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.

What's included

1 assignment8 plugins

1 assignmentTotal 15 minutes

End of module quiz15 minutes

8 pluginsTotal 146 minutes

Overview1 minute
Network anomaly detection5 minutes
K nearest neighbours30 minutes
K nearest neighbours for outlier detection20 minutes
Network anomaly detection using machine learning15 minutes
Outlier detection using K nearest neighbours15 minutes
Outlier detection using one class SVM15 minutes
Detecting normal and attack traffic45 minutes

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.