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This research applies Partially Observable Markov Decision Processes (POMDP) and Proximal Policy Optimization (PPO) to automate intrusion response strategies for Operational Technology systems. By leveraging traffic measurements to model partial observability, the study demonstrates effective learning-based defenses against various MITRE attacks in an emulated industrial environment. The work bridges critical infrastructure security with advanced reinforcement learning techniques, offering a tractable framework for real-time threat mitigation.
Learning Intrusion Response Strategies for OT Systems
arXiv:2609.10298v1 Announce Type: cross Abstract: Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential societal services. For this reason, developing automated intrusion response strategies is highly important. In this paper, we present a formal model of an OT intrusion response use case using the POMDP framework. It includes a realistic model of partial observability that is based on traffic measurements. This approach allows us to develop tractable, learning-based solution methods for automated intrusion response, which are based on PPO. We evaluate the obtained response strategies on an emulated OT system and find that they are effective against several types of MITRE attacks for the studied use case.
Abstract:Cyberattacks against Operational Technology (OT) systems, which monitor and control industrial processes, pose an increasing threat to essential societal services. For this reason, developing automated intrusion response strategies is highly important. In this paper, we present a formal model of an OT intrusion response use case using the POMDP framework. It includes a realistic model of partial observability that is based on traffic measurements. This approach allows us to develop tractable, learning-based solution methods for automated intrusion response, which are based on PPO. We evaluate the obtained response strategies on an emulated OT system and find that they are effective against several types of MITRE attacks for the studied use case.
Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
arXiv:2609.10298 [cs.CR]
(or arXiv:2609.10298v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2609.10298 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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