MSc Cyber Security and Artificial Intelligence

This module introduces you to the principles, architectures, and applications of deep machine learning, with a focus on how modern neural networks can model complex, high-dimensional data. Throughout the module, you will explore key concepts such as neural network design, backpropagation, optimisation strategies, regularisation techniques, and model evaluation. You will study a range of architectures, including feedforward neural networks, convolutional neural networks, recurrent neural networks, and transformer-based models, and consider their strengths and limitations across different domains. A strong practical emphasis is maintained through hands-on implementation and experimentation using industry-standard tools such as Python and deep learning frameworks (e.g., TensorFlow or PyTorch). You will work with real-world datasets to design, train, and evaluate models, gaining experience in debugging, tuning, and improving performance. Consideration is also given to the ethical and societal implications of deep learning, including issues of bias, interpretability, and responsible deployment. By the end of the module, you will have developed both a rigorous conceptual understanding of deep machine learning and the practical skills needed to design, implement, and critically evaluate deep learning solutions in a variety of applied contexts.

This module provides in-depth knowledge and practical skills in ethical hacking, penetration testing and vulnerability assessment. Students will explore the principles of offensive security and learn how to identify, analyse and exploit vulnerabilities in systems, networks and applications in a lawful and controlled manner. Students will develop skills in reconnaissance, scanning, exploitation, post-exploitation and security evaluation. The module also highlights the importance of understanding human factors, including social engineering and attacker behaviour. Through lectures and hands-on lab activities, students will apply penetration testing methodologies to realistic scenarios, gaining experience in identifying weaknesses and recommending appropriate mitigation strategies. Ethical, legal and professional considerations are embedded throughout the module to ensure responsible practice in offensive cybersecurity.

This module introduces you to the fast moving field of Generative AI and helps you develop the knowledge and practical skills needed to use it critically, creatively, and responsibly. You will explore how systems such as large language models work, where they are useful, and where their limitations lie. The module gives you a clear grounding in key ideas such as tokens, embeddings, transformers, model training, fine tuning, and alignment, but in a way that focuses on understanding and application. As the module progresses, you will learn how to design effective prompts, evaluate AI outputs, and think carefully about how people interact with conversational AI systems. You will also explore techniques such as retrieval augmented generation (RAG), model adaptation, and agent based workflows, helping you understand how generative AI can be connected to tools, data, and real world tasks. Alongside the technical content, the module places strong emphasis on responsible and professional use of Generative AI. You will examine issues such as bias, misinformation, privacy, intellectual property, security risks, and safe system design, so that you can make informed judgements about when and how these technologies should be used. The module is practical, applied, and forward looking. It is designed to help you build confidence in working with Generative AI in academic, professional, and research contexts, while also encouraging you to question claims, test ideas, and engage with emerging developments in this rapidly changing area. By the end of the module, you will be able to understand core Generative AI concepts, experiment with key techniques, and critically evaluate the opportunities and risks of using these technologies in practice.

This module provides you with the knowledge and practical skills required to manage, respond to, and investigate cybersecurity incidents in modern organisational environments. You will explore the full incident response lifecycle, from preparation and detection through to containment, recovery, and post-incident learning. You will work both individually and collaboratively, taking on defined professional roles within an incident response team (e.g., Incident Manager, Forensic Analyst, Legal Advisor). Through realistic scenarios, you will apply digital forensic techniques, analyse evidence, and make decisions under pressure while considering legal, ethical, and organisational constraints. The module emphasises industry practices, teamwork, and professional responsibility, preparing you for roles in security operations centres (SOCs), CERT teams, and digital investigation units. You will also develop communication skills through reporting and presenting findings to both technical and non-technical stakeholders.

This module explores how organisations protect, govern and assure information in modern digital environments. You will study how information-related risks are identified, assessed and managed across systems that store, process and transmit data. The module introduces the core principles of information assurance, including confidentiality, integrity, availability, authenticity and non-repudiation, and shows how these are applied through governance, risk and compliance practices. You will examine the policies, standards, frameworks and regulatory requirements that shape information assurance in contemporary organisations. The module also considers how assurance applies beyond technical controls alone, including legal, organisational, physical and human factors. Through case studies and applied activities, you will learn how to evaluate information assets, classify risks, and propose suitable assurance and governance measures for complex and evolving environments such as smart systems, critical infrastructure and interconnected digital services. By the end of the module, you will be able to connect theory with practice and develop informed, responsible and risk-aware approaches to protecting organisational information and supporting secure decision-making.

This module introduces you to the core concepts, methods, and applications that underpin Artificial Intelligence. It explores how intelligent systems are designed to perceive information, represent knowledge, reason about problems, learn from data, and make decisions in complex environments. The module provides a foundation for understanding both classical and contemporary approaches to AI, including search, optimisation, knowledge representation, reasoning, uncertainty, and machine learning. You will examine the principles that allow AI systems to solve problems, support decision making, and operate in real-world contexts. Alongside the technical foundations, you will also consider the strengths, limitations, and wider implications of Artificial Intelligence, including issues of ethics, responsibility, and professional practice. By the end of the module, you will have developed a broad understanding of the key principles of AI and how they are applied across a range of domains. This module provides an important foundation for further study in specialist areas such as machine learning, data science, robotics, intelligent systems, and generative AI.

This module prepares you to design, plan, and justify a substantial Masters level project in a computing related discipline. It introduces the principles of research design, academic and professional enquiry, and project planning in areas such as Computer Science, Data Science, Artificial Intelligence, and Cyber Security. You will learn how to identify and refine a suitable research or development problem, review and evaluate relevant literature, formulate research questions or project aims, and select appropriate methods for investigation. The module will also help you develop the professional skills needed to manage a project effectively, including planning, risk awareness, ethical consideration, communication, and reflective practice. Alongside research methods, the module explores professionalism in computing, including ethical and responsible practice, legal and regulatory awareness, data and AI ethics, cyber responsibility, and professional standards expected in industry and research environments. You will consider how computing professionals design and conduct work in ways that are rigorous, responsible, and aligned with the needs of stakeholders. The assessment for the module is a research proposal in which you will define and justify a project idea, demonstrate awareness of relevant literature and methods, and present a realistic project plan. This will help prepare you for your final MSc project and for professional practice in technical and research led roles.

This module introduces the principles, technologies and practical techniques used to protect modern digital and networked infrastructure. Students will explore how secure communication, perimeter defence, access control, threat modelling, intrusion detection, wireless security and trust services contribute to multiple layers of protection across organisational environments. Through lectures, workshops and applied coursework, students will examine threats, vulnerabilities and security controls, develop appropriate mitigation strategies for realistic organisational scenarios, and justify security decisions with reference to relevant legal and ethical requirements.