State-of-the-art industrial control systems (ICS) and edge-to-cloud technology require automated testing mechanisms to cope with the ever-increasing attack vectors. Embark on a journey into the exciting world of artificial intelligence to strengthen industrial operations against cyber threats with our BSc/MSc final year project. Our focus is on the use of advanced techniques such as reinforcement learning (RL) to continuously manage strong resilience of industrial control systems. This unique approach not only improves resilience to cyber-attacks, but also helps build capacity in security testing teams. Help us to better protect our company's production facilities to ensure a safer and more connected future.
Student Target Groups:
- Students of ICE/Telematics;
- Students of Computer Science;
- Students of Software Engineering.
Thesis Type:
- Master Thesis / Bachelor Thesis
Goal and Tasks:
The aim of this work is to apply state-of-the-art RL methods to a defined setup of an ICS based on ROS/ROS2 systems in order to detect potential vulnerabilities of such a system. Therefore, MITRE ATT&CK®, a globally accessible knowledge base of adversary tactics and techniques based on real-world observations, is used as a foundation for the development of specific threat models and methodologies and used by a RL based automated security testing.
- Thorough literature research on the topic;
- Select suitable RL methods;
- Design reward function and conduct experiments to investigate the applicability of RL based automated testing;
- Summarize the results in a written report, and prepare an oral presentation.
Recommended Prior Knowledge:
- Programming skills in Python;
- Prior experience with deep learning frameworks is desirable (preferably PyTorch).
- Interest in the topic.
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