Programme/Approved Electives for 2025/26
None
Available as a Free Standing Elective
No
For security to be effective, it must be usable and designed to work for people. This module sits at the intersection of usability and security and covers areas such as usable security, human-centred design principles in cyber security, Operating Systems and Virtualisation Security, Secure and Usable Software and Application, risk management, AI in cyber security, policy and ethics of usable security. We will look at the influence of human factors such as social engineering and usability on security, along with behavioural psychology in security, and training issues; giving you a broader view of the area, beyond standard approaches.
Aims
This module aims to enable students to:• Gain an understanding of the role human factors and behavioural psychology play in cybersecurity and how to design security systems that are acceptable and usable to a variety of users• Learn key human-centred design principles in cybersecurity to help in reasoning and designing security solutions – systems, tools, techniques, practices – that are effective and easy for end-users to understand, adopt, and apply• Establish a basic understanding of core cybersecurity concepts, including systems, threats, vulnerabilities, attack vectors, impacts, security risk management, socio-technical systems, social engineering tactics, and human error causes• Learn and apply foundational artificial intelligence (AI) concepts and approaches to cybersecurity, covering machine learning types, data sources, and AI techniques for various cybersecurity applications, including intrusion detection, malware detection, and classification• consider broader organisational and societal perspectives on security, emphasising the importance of trust and collaboration for effective cybersecurity• Learn how to engage stakeholders and negotiate security solutions that meet their needs• Critically analyse the human factors influencing cybersecurity systems, design usable security tasks and awareness training campaigns, and apply artificial intelligence techniques for achieving or enhancing cybersecurity objectives
Intended Learning Outcomes
Explain basic components of threat modelling and vulnerability analysis and demonstrate their application on cybersecurity systems: 1Evaluate different security risk management approaches and design the risk assessment for specific use case scenarios: 1,2Appraise the capabilities and limitations of target users, the devices they use, and develop an effective approach to encourage a positive security culture by enhancing security awareness and behaviour change: 1Design well-fitting security tasks that consider mental and physical workload by evaluating them using system usability test metrics: 1Apply basic machine learning approaches for achieving or enhancing cyber security objectives: 2Evaluate literature and views related to usable cyber security and synthesise into summary insights: 1,2
Lectures: - 36 hours [6 hours each per week]Practicals/Tutorials: - 36 hours [6 hours each per week]Private Study: - 106 hours Assessment Preparation: - 122 hours
Description of Module Assessment
1: Group Assessment weighted 60%Weekly group-based tasksThe assessment involves completing 6 weekly group-based tasks and submitting a short weekly report (2000 words per group per week equivalent, with 4 members in a group contributing 500 each week) covering the learning from each week to build a portfolio of work. Tasks will be a combination of hands-on practicals and design/evaluation based tasks e.g. security risk management approaches.
If students do not engage with the group-based tasked, penalties will be given where appropriate reasons for non-engagement are not given. This will be reviewed on a weekly basis by the module teaching team.
2: Assignment weighted 40%Cyber Security ReportThe assessment involves completing a project where the students will be required to (1) design a cyber security awareness campaign that can help to improve awareness of evolving cyber security risks and countermeasure (1500 words), and (2) apply machine learning technique(s) for addressing a cyber threat use case scenario (500 words plus code and results). These will include writing a summary reflection of the lessons learned during the module and any challenges that they might have faced. These are expected to be written up into a report (2000 words maximum) and submitted.