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Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning | IEEE Conference Publication | IEEE Xplore

Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning


Abstract:

Industrial control systems are subject to cyber attacks that produce physical consequences. These attacks can be both hard to detect and protracted. Here, we focus on dec...Show More

Abstract:

Industrial control systems are subject to cyber attacks that produce physical consequences. These attacks can be both hard to detect and protracted. Here, we focus on deception-based sensor bias attacks made against a hierarchical control system where the attacker attempts to be stealthy. We develop a data-driven, optimization-based attacker model and use the Koopman operator to represent the system dynamics in a domain-aware and computationally efficient manner. Using this model, we compute several different attacks against a high-fidelity commercial building emulator and compare the impacts of those attacks to each other. Finally, we discuss some computational considerations and identify avenues for future research.
Date of Conference: 26-29 September 2022
Date Added to IEEE Xplore: 20 December 2022
ISBN Information:
Conference Location: National Harbor, MD, USA

Funding Agency:


References

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