All Outputs (3)

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.

OGMA: Visualisation for software container security analysis and automated remediation (2022)
Conference Proceeding
Mills, A., White, J., & Legg, P. (2022). OGMA: Visualisation for software container security analysis and automated remediation. In 2022 IEEE International Conference on Cyber Security and Resilience (CSR) (76-81). https://doi.org/10.1109/CSR54599.2022.9850335

The use of software containerisation has rapidly increased in academia and industry which has lead to the production of several container security scanning tools for assessing the security posture and threat of a container image. The variability betw... Read More about OGMA: Visualisation for software container security analysis and automated remediation.

Efficient and interpretable real-time malware detection using random-forest (2019)
Conference Proceeding
Mills, A., Spyridopoulos, T., & Legg, P. (2019). Efficient and interpretable real-time malware detection using random-forest. . https://doi.org/10.1109/CyberSA.2019.8899533

© 2019 IEEE. Malicious software, often described as malware, is one of the greatest threats to modern computer systems, and attackers continue to develop more sophisticated methods to access and compromise data and resources. Machine learning methods... Read More about Efficient and interpretable real-time malware detection using random-forest.