Applied AI for Cybersecurity

Welcome to the course website for Applied AI for Cybersecurity.

This module introduces final-year undergraduate students to the practical use of artificial intelligence in cybersecurity. It explores how AI and machine learning can support tasks such as intrusion detection, phishing analysis, malware-related analytics, alert triage, and security monitoring. It also examines the risks of using AI in cyber systems, including adversarial attacks, model misuse, unreliable outputs, and security weaknesses in large language model applications.

The course is designed as an applied, critical, and professionally relevant module. Students are expected not only to use AI techniques, but also to evaluate them carefully, understand their limits, and judge whether they are suitable for real deployment.

What this course is about

This module sits at the intersection of:

  • cybersecurity;
  • machine learning;
  • security analytics;
  • trustworthy AI;
  • generative AI in operational environments.

Across five teaching weeks, the course moves from foundations and data-driven thinking to practical modelling, modern AI-assisted cyber workflows, attacks against AI systems, and trustworthy deployment.

Course structure

The module is organised around five themes:

  1. Foundations of Applied AI for Cybersecurity
    Understanding where AI fits in cyber defence, what kinds of data are used, and why careful problem framing matters.

  2. Data, Features, and Classical Machine Learning for Security Analytics
    Working with cybersecurity datasets, preprocessing, feature engineering, model evaluation, and classical ML workflows.

  3. Deep Learning and Generative AI in Cybersecurity
    Exploring deep learning for cyber data and the role of LLMs and GenAI in security operations.

  4. Attacking and Defending AI Systems
    Studying adversarial machine learning, poisoning, evasion, prompt injection, and secure AI design.

  5. Trustworthy Deployment, Governance, and Capstone Case Study
    Bringing the module together through explainability, robustness, privacy, governance, and deployment judgement.

Who this course is for

This course is intended for Level 6 students in computing, cybersecurity, networking, or related programmes.

Students are expected to have:

  • basic knowledge of computer networks and cybersecurity;
  • introductory Python programming skills;
  • a willingness to engage with practical analysis and critical discussion.

How to use this website

Use the pages in the left-hand navigation to move through the module materials.

Start here

Weekly lectures

Supporting pages

Learning approach

The teaching approach in this module combines:

  • lectures for concepts and structured explanations;
  • labs for practical experimentation;
  • discussion for critique, interpretation, and professional judgement;
  • independent reading and reflection.

The module emphasises a simple but important principle:

AI in cybersecurity should not be trusted just because it is technically impressive.

Students are encouraged to ask:

  • What problem is being solved?
  • What data is available?
  • What mistakes matter most?
  • How should the system be evaluated?
  • What could go wrong after deployment?
  • Should the system actually be trusted in practice?

Practical focus

The practical side of the module includes work on:

  • cybersecurity datasets;
  • baseline modelling and classical ML workflows;
  • evaluation metrics for security tasks;
  • AI-assisted analysis and critique;
  • threat modelling of AI-enabled systems;
  • deployment review and recommendation.

The goal is not only to build technical familiarity, but also to develop sound judgement.

Responsible use of AI

This module treats AI as both:

  • a useful tool for cybersecurity; and
  • a source of new security, reliability, and governance challenges.

Students should engage with AI critically, responsibly, and transparently. Any use of AI tools in coursework must follow university policy and module guidance. Students remain responsible for the correctness, originality, and integrity of their own work.

Suggested path through the site

A sensible order for students is:

  1. read the Syllabus;
  2. check the Schedule;
  3. work through the weekly lecture pages in order;
  4. follow the Labs alongside the lectures;
  5. review the Assessment requirements early;
  6. use the References page for deeper reading.

Closing note

Applied AI for Cybersecurity is a rapidly evolving area. This course is designed to help students build a clear, practical, and critical understanding of the field so they can use AI thoughtfully rather than uncritically in future cybersecurity work.

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