Cyber security and computing
Introduction
The Cyber Security and Computing research group is a team of dedicated researchers on a mission to explore the frontiers of cyber security and advanced computing technologies. We leverage our combined expertise in cyber security and cutting-edge computing techniques to forge innovative solutions that drive the field forward.
Our core areas of research include:
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Cyber security
We develop and analyse cutting-edge security technologies to safeguard systems, networks and data from cyber attacks. Our research areas include secure system design, network security, computing security and digital forensics.
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Advanced computing
We explore the potential of cutting-edge computing technologies like cloud computing, Internet of Things, blockchain, edge computing and communication systems. We also investigate how advanced computing techniques like artificial intelligence and data analytics can be leveraged to advance real-life applications including cyber security.
Group members
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Professor Wei Jie
Professor of ComputingSchool of Computing and EngineeringProfessor Wei Jie has been actively conducting research in a broad spectrum of computing, mainly on distributed computing, data analytics, and computing security. Professor Jie has published about 70 academic papers on mainstream international journals and international conferences/workshops and edited 3 books. Professor Jie has a series of successful records of attracting substantial research funding from UK funding bodies such as the Royal Society, Innovate UK, JISC (Joint Information Systems Committee), AWS (Amazon Web Services), etc.
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Dr Abel Yeboah-Ofori
Associate Professor in Cyber Security
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Dr Alireza Esfahani
Senior Lecturer in Cyber Security
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Dr Shidrokh Goudarzi
Lecturer in Computer Science
Current projects
AI-powered automotive API security systems for connected and autonomous vehicles
Funded by the Innovate UK CyberASAP (Cyber Security Academic Startup Accelerator Programme) Programme, this project proposes an AI-driven solution focused on enhancing automotive cyber security, particularly within automakers' application programming interfaces (APIs). Key components of this approach include vulnerability detection and analysis, threat prediction and prevention, anomaly detection and response and continuous learning and improvement.
Developing and implementing ensemble machine learning models
Funded by the Innovate UK Knowledge Transfer Partnerships (KTP) Programme, this 18-month project aims to develop and implement ensemble machine learning models to improve the overall performance of the evoML platform commercialised by the business partner TurinTech Limited.
Furthermore, novel frameworks for explainable rule-based ensemble algorithms will be developed and implemented to improve the functionality of evoML.
Cyber security training and workshops for local authority staff
Funded by UWL's Knowledge Exchange Seed Fund, this project will deliver cyber security training and workshops. The training will educate and raise awareness for all staff and stakeholders in local authorities. It will equip participants with knowledge of the cyber threat landscape and the methods attackers use to exploit local authority systems. In addition to the training, the project also aims to develop and pilot CPD courses.
Past projects
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IoT Armor: An IoT secure-by-default educational toolkit
Funded by the Innovate UK CyberASAP (Cyber Security Academic Startup Accelerator Programme) Programme (2023/24), this project presents IoT-Armor which is an educational toolkit that democratises the knowledge around security-by-default principles for IoT devices, thus facilitating the compliance needs laid out in the government's code of practice for consumer IoT devices. The toolkit empowers users to test, learn and implement crucial security measures such as secure boot, remote attestation, device tamper detection, secure communication and device authentication.
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ROS-PCon: Attack preventive control for robotics
Funded by the Innovate UK CyberASAP (Cyber Security Academic Startup Accelerator Programme) Programme (2022/23), this project presents a solution that helps to secure the compromise of robotic systems. The system developed learns the robotic movement through the robot execution feedback based on deep learning technology while the robot is performing its role in a controlled, best-case environment. It can then highlight anomalies when it spots something that deviates from the control.
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Non-invasive 3D LiDAR human activity analysis in e-healthcare
Funded by UWL's Knowledge Exchange Seed Fund (2022/23), this project aims to develop AI models by introducing LiDAR (Light Detection and Ranging) sensors to collect, detect, process, analyse and predict human activities and other healthcare related behaviour patterns. The work will help to keep the elderly and vulnerable people as safe and as well as possible whilst remaining in their own homes.
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Geospatial data analytics for improving community safety
Funded by UWL's Knowledge Exchange Seed Fund (2021/22), this project created a machine learning based solution to improve public safety by the tracking and analytics of geospatial data. The work will benefit local authorities on strategy planning and policy making to effectively improve public safety.
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Parallel and distributed methods for community detection over large scale dynamic networks
Funded by the UK Royal Society International Exchange programme (2022/23), this project focused on fast and efficient parallel and distributed methods for real-time community detection over large-scale dynamic networks.
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A self-organising community detection algorithm for large-scale social network
Funded by the Amazon Research Grant program, this project presented a self-organising community detection algorithm for large-scale networks based on the idea of swarm intelligence. The results help us understand the behaviours of individual members in a large social network, interaction models between members and the properties of belonging communities. The algorithm and tool will be tested and evaluated on the Amazon cloud platform.
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A proxy credential auditing infrastructure for the UK e-science National Grid service
Funded by the UK Joint Information Systems Committee (JISC), this project developed a proxy certificate auditing infrastructure and demonstrated a solution that enables thorough auditing and monitoring of proxy credential usage in widely distributed and heterogeneous research environments exemplified by the UK National Grid service.
Research publications
- Each member's profile provides a list of their publications
- The UWL Repository offers a searchable collection of publications by our members
Training programmes
The research group is committed to fostering the next generation of leaders in our research themes. UWL offers comprehensive PhD, MSc, and BSc programs designed to equip students with the technical expertise and research skills.
Our BSc Cyber Security course is designed to train tomorrow’s security professionals, combining fundamental concepts and principles with exposure to new technologies and solutions. Students will gain a practical understanding of key issues relating to the design, analysis and implementation of modern IT security systems.
Our MSc Cyber Security course delves deeper into advanced topics in cyber security. It provides professional education in both the theory and practice of cyber security, and helps students to gain comprehensive knowledge and critical skills of cyber security in both technical and human dimension.
We welcome applications from highly motivated students interested in conducting PhD study aligned with our group's research themes. Visit the School of Computing and Engineering research degrees page to explore potential topics of study. Please contact the relevant faculty members affiliated with the research group.
Research degrees with the School of Computing and Engineering
PhD research degrees with the School of Computing and Engineering
Find out more
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Research Centres and Groups
Find out about our multi-disciplinary areas of expertise, PhD research, and teaching.
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Research impact
Learn how our PhD research has helped communities locally, nationally and internationally.
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The Graduate School
If you are interested in studying for a PhD or Professional Doctorate, the Graduate School is here to support your research.
