Digital twins of cyber physical systems in smart manufacturing for threat simulation and detection with deep learning for time series classification (2024)
Presentation / Conference
Lo, C., Win, T. Y., Rezaeifar, Z., Khan, Z., & Legg, P. (2024, August). Digital twins of cyber physical systems in smart manufacturing for threat simulation and detection with deep learning for time series classification. Paper presented at The 29th International Conference on Automation and Computing (ICAC2024), University of Sunderland, UK

With increasing reliance on Cyber Physical Systems (CPS) for automation and control in Industry 4.0 and 5.0, ensuring their security against cyber threats has become paramount. Traditional security mechanisms, constrained by operational continuity an... Read More about Digital twins of cyber physical systems in smart manufacturing for threat simulation and detection with deep learning for time series classification.

Vulnerability detection through machine learning-based fuzzing: A systematic review (2024)
Journal Article
Bamohabbat Chafjiri, S., Legg, P., Hong, J., & Tsompanas, M. A. (2024). Vulnerability detection through machine learning-based fuzzing: A systematic review. Computers and Security, 143, Article 103903. https://doi.org/10.1016/j.cose.2024.103903

Modern software and networks underpin our digital society, yet the rapid growth of vulnerabilities that are uncovered within these threaten our cyber security posture. Addressing these issues at scale requires automated proactive approaches that can... Read More about Vulnerability detection through machine learning-based fuzzing: A systematic review.

TRIST: Towards a container-based ICS testbed for cyber threat simulation and anomaly detection (2024)
Conference Proceeding
Lo, C., Christie, J., Win, T. Y., Rezaeifar, Z., Khan, Z., & Legg, P. (in press). TRIST: Towards a container-based ICS testbed for cyber threat simulation and anomaly detection. In Springer Proceedings in Complexity book series

Cyber-attacks on Industrial Control Systems (ICS), as exemplified by the incidents at the Maroochy water treatment plant and the Ukraine's electric power grid, have demonstrated that cyber threats can inflict significant physical impacts. These incid... Read More about TRIST: Towards a container-based ICS testbed for cyber threat simulation and anomaly detection.

Cyber Funfair: Creating immersive and educational experiences for teaching Cyber Physical Systems Security (2024)
Conference Proceeding
Mills, A., White, J., & Legg, P. (2024). Cyber Funfair: Creating immersive and educational experiences for teaching Cyber Physical Systems Security. In SIGCSE 2024: Proceedings of the 55th ACM Technical Symposium on Computer Science Education (847-852). https://doi.org/10.1145/3626252.3630820

Delivering meaningful and inspiring cyber security education for younger audiences can often be a challenge due to limited expertise and resources. Key to any outreach activity is that it both develops a learner's curiosity, as well as providing educ... Read More about Cyber Funfair: Creating immersive and educational experiences for teaching Cyber Physical Systems Security.

Digital twins in industry 4.0 cyber security (2024)
Conference Proceeding
Lo, C., Win, T. Y., Rezaeifar, Z., Khan, Z., & Legg, P. (2024). Digital twins in industry 4.0 cyber security. In Proceedings of the IEEE Smart World Congress 2023. https://doi.org/10.1109/swc57546.2023.10449147

The increased adoption of sophisticated Cyber Physical Systems (CPS) in critical infrastructure and various aspects of Industry 4.0 has exposed vulnerabilities stemming from legacy CPS and Industrial Internet of Things (IIoT) devices. The interconnec... Read More about Digital twins in industry 4.0 cyber security.

Evaluating data distribution strategies in federated learning: A trade-off analysis between privacy and performance for IoT security (2023)
Conference Proceeding
White, J., & Legg, P. (in press). Evaluating data distribution strategies in federated learning: A trade-off analysis between privacy and performance for IoT security.

Federated learning is an effective approach for training a global machine learning model. It uses locally acquired data without having to share local data with the centralised server. This method provides a machine learning model beneficial for all p... Read More about Evaluating data distribution strategies in federated learning: A trade-off analysis between privacy and performance for IoT security.

Longitudinal risk-based security assessment of docker software container images (2023)
Journal Article
Mills, A., White, J., & Legg, P. (2023). Longitudinal risk-based security assessment of docker software container images. Computers and Security, 135, Article 103478. https://doi.org/10.1016/j.cose.2023.103478

As the use of software containerisation has increased, so too has the need for security research on their usage, with various surveys and studies conducted to assess the overall security posture of software container images. To date, there has been v... Read More about Longitudinal risk-based security assessment of docker software container images.

Federated learning: Data privacy and cyber security in edge-based machine learning (2023)
Book Chapter
White, J., & Legg, P. (2023). Federated learning: Data privacy and cyber security in edge-based machine learning. In C. Hewage, Y. Rahulamathavan, & D. Ratnayake (Eds.), Data Protection in a Post-Pandemic Society (DPPPS) – Best Practices, Laws, Regulations, and Recent Solutions. Springer. https://doi.org/10.1007/978-3-031-34006-2

Machine learning is now a key component of many applications for understanding trends and characteristics within the wealth of data that may be processed, whether this be learning about customer preferences and travel preferences, forecasting future... Read More about Federated learning: Data privacy and cyber security in edge-based machine learning.

Static analysis of security issues of the Python packages ecosystem (2023)
Journal Article
Gorine, A., & Spondon, F. (2023). Static analysis of security issues of the Python packages ecosystem. World Academy of Science, Engineering and Technology, 17(3), 33-40

Python is considered the most popular programming language and offers its own ecosystem for archiving and maintaining open-source software packages. This system is called the python package index (PyPI), the repository of this programming language. U... Read More about Static analysis of security issues of the Python packages ecosystem.

Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications (2020)
Journal Article
Souri, A., Mohammed, A. S., Yousif Potrus, M., Malik, M. H., Safara, F., & Hosseinzadeh, M. (2020). Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications. IEEE Access, 8, 23863-23874. https://doi.org/10.1109/ACCESS.2020.2967629

By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT... Read More about Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications.

Performance analysis of IEEE 802.11a and IEEE 802.11g under Bursty traffic (2016)
Journal Article
Hussain Malik, M., Raza, S., Ilias Talukder, A. B. M., & Rafi, M. A. (2016). Performance analysis of IEEE 802.11a and IEEE 802.11g under Bursty traffic. Communications and Network, 14(10),

Technology from the end of last millennium, has been shifting with an unprecedented velocity scaling human anxiety down; communication technology, perhaps demonstrated the highest. Within the frontier of such profundities, reclines a key concern; Qua... Read More about Performance analysis of IEEE 802.11a and IEEE 802.11g under Bursty traffic.