Master of Science in Cyber Security [MSc(CS)] [*Subject to University’s Approval]

Foundations of Cyber Security

This course provides a comprehensive introduction to the fundamental principles, concepts, and practices of cyber security. Students will explore the core tenets of information assurance - confidentiality, integrity, and availability (the CIA triad) - and examine how these principles are operationalised in modern digital infrastructures. Key topics include cryptographic foundations, authentication and access control mechanisms, network security architectures, common vulnerability taxonomies, and the anatomy of typical cyber-attacks. The course adopts a risk-aware mindset from the outset, introducing students to threat modelling and the concept of defence-in-depth. Through case studies and class discussion, students will possess a robust conceptual vocabulary in cyber security, enabling them to engage critically with more specialised security domains.

Information Security and Risk Management

This course addresses the strategic, managerial, and governance dimensions of information security. The curriculum is structured around the risk management lifecycle: asset identification and valuation, threat and vulnerability assessment, risk analysis, risk treatment strategies, and residual risk monitoring. Students will gain proficiency in internationally recognised standards and frameworks, including ISO/IEC 27001, the NIST Cybersecurity Framework, and COBIT. Critical topics include: security policy development and enforcement, incident response planning, security awareness training programme design, and the legal, regulatory. The course emphasises the role of the security function, teaching students to communicate risk and strategic terms.

Topics in Security and Privacy

This seminar-style course examines emerging and specialised topics at the frontiers of security and privacy research and practice. The curriculum is intentionally dynamic, adapting to the rapidly evolving threat and technology landscape. Core thematic clusters include: (a) Privacy-Enhancing Technologies (PETs) , such as differential privacy, federated learning, and their practical deployment challenges; (b) Adversarial Machine Learning, such as poisoning attacks, evasion attacks, model inversion, and defensive techniques for securing AI/ML pipelines; (c) Blockchain and Decentralised Systems Security, including smart contract vulnerabilities, consensus mechanism attacks, and decentralised identity; (d) Sociotechnical Dimensions of Security, including usability and human factors in security, security economics, digital sovereignty, and the tension between surveillance and civil liberties. The course cultivates the intellectual agility required to anticipate and respond to novel threats that do not yet have established mitigations.

Data Communication Security

This course provides an in-depth introduction of the protocols, architectures, and cryptographic mechanisms that secure data in transit across modern communication networks. The curriculum follows the layered structure of the TCP/IP protocol stack, systematically analysing security threats and countermeasures at each layer. Topics include: link-layer security, network-layer security, transport-layer security, and application-layer security. The course also addresses the security challenges of emerging communication paradigms, including 5G network security architectures, software-defined networking (SDN) security, and the implications of quantum computing for cryptographic protocols. Students will learn to inspect, configure, and secure communication channels against eavesdropping, tampering, and impersonation attacks.

Database System and Management

This course provides an in-depth exploration of database systems and management practices. Students will learn about database design principles, relational algebra, SQL for data manipulation, and data warehouse. The course covers both relational databases and NoSQL systems, emphasising their use in managing large-scale big data. Topics include transaction management and concurrency control, data integrity, and performance optimisation. Students will gain hands-on experience in designing and implementing database solutions, essential for effective big data management in diverse application scenarios.

Generative Artificial Intelligence and Applications

This course focuses on generative AI models and their applications across various domains. Students will explore concepts such as generative adversarial networks (GANs), variational autoencoders, and large language models like GPT. The course emphasises the development and implementation of generative AI systems for tasks such as image synthesis, text generation, and creative content creation. Students will gain hands-on experience with generative AI frameworks and tools, preparing them to innovate and develop applications that leverage generative AI techniques.

AI Security and Ethical Risks

This course examines the dual relationship between artificial intelligence and cyber security: securing AI systems themselves, and understanding the ethical risks AI introduces into society. Students will explore the adversarial threat landscape targeting machine learning pipelines, including data poisoning, model evasion, model inversion, and membership inference attacks, alongside defensive techniques such as adversarial training, differential privacy, and model verification. In parallel, the course critically interrogates the broader ethical and societal dimensions of AI deployment: algorithmic bias and fairness, transparency and explainability (XAI), accountability in automated decision-making, the implications of large language models for misinformation and disinformation, and the development of responsible AI governance frameworks. Through case studies of real-world AI failures and security breaches, students will learn to conduct algorithmic impact assessments and to design AI systems that are not only technically robust but also ethically defensible and aligned with emerging regulatory requirements.

Computational Thinking and Coding 

This course provides a foundational introduction to computational thinking as a problem-solving methodology and to coding as its practical implementation. It is designed for students without prior programming experience and serves as the gateway to all subsequent technical coursework. The curriculum is structured around the four pillars of computational thinking. Students will learn to implement these concepts using a high-level programming language (Python). Assessment is project-based, requiring students to apply computational thinking to authentic security-related problems. Besides, students will be introduced to a variety of teaching and learning approaches to use coding to develop computational thinking, with the effectiveness of these approaches critically examined.

Cloud Security and Data Protection 

This course addresses the security challenges and data protection imperatives arising from the widespread adoption of cloud computing architectures. It adopts a shared responsibility model perspective, clarifying the delineation of security obligations between cloud service providers and cloud customers. Key technical topics include cloud architecture security, identity and access management (IAM) in cloud-native contexts, container and serverless security, configuration management and infrastructure-as-code security, DevSecOps practices, and cloud-native logging, monitoring, and incident response. Case studies of major cloud security incidents and misconfigurations will be dissected.

Privacy, Security, and Policy

This course centers on the pivotal aspects of privacy, security, and policy in artificial intelligence. Students will explore the intricate balance between innovative AI development and the ethical, legal, and social implications that accompany these advancements. The course includes in-depth discussions on data privacy, highlighting how AI impacts data collection, storage, and usage while considering the privacy of individuals. Security aspects cover the vulnerabilities and threats inherent in AI systems and strategies to mitigate such risks. The course will also discuss AI governance, examining current and emerging policies and regulations at both national and international levels. Case studies of security incidents from diverse organizations will be discussed. Implications of case studies on policy in AI for organizational change and diffusion of innovation will be explored.

Data Mining for Trends Analysis and Forecasting

This course provides an overview of data mining and the fundamental concepts of education. Data mining is increasingly being used to improve teaching and learning process and educational pedagogy. The course also provides a comprehensive understanding of probability and statistics, which are fundamental in developing and validating models for association, classification, and clustering. These models are crucial for identifying patterns and predicting future trends based on historical data. It also covers the concepts of STEM education for students to design STEM learning activities and discuss the social and moral issues related to STEM education. 

Capstone Project in Cyber Security 

The capstone project allows students to conduct independent research on a topic of their choice within cyber security. Students will apply the knowledge and skills acquired throughout the program to investigate a research question, designing novel solutions that contribute new insights to the field and potentially lead to a publication. The capstone project involves a comprehensive written report and an oral defense, demonstrating the student's ability to design, implement, and evaluate cyber security solutions. The capstone project provides an extended, applied learning experience: deliverables may take the form of a security assessment, a working prototype or security tool, or an empirical study, enabling students to build a portfolio of demonstrable technical work for career purposes. Each student will receive regular individual supervision totalling no fewer than 12 to 15 hours across the project period, and supplemented by scheduled progress seminars and technical workshops organised by the programme team. In particular, all students undertaking the capstone project must complete a mandatory research methods workshop series (6 to 9 hours) at the start of the capstone project, covering research design, literature search and review, research methods in cyber security, research ethics, and academic writing, so as to strengthen their research capability before the assessed proposal stage.