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 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.
Published: January 31, 2023
Citation
Bakker C., A. August, S. Huang, S.S. Vasisht, and D.L. Vrabie. 2022.Deception-Based Cyber Attacks on Hierarchical Control Systems using Domain-Aware Koopman Learning. In Resilience Week (RWS 2022), September 26-29, 2022, National Harbor, MD, 1-8. Piscataway, New Jersey:IEEE.PNNL-SA-173605.doi:10.1109/RWS55399.2022.9984030