Document Type

Article

Abstract

Cyberattacks may be performed on process control systems due to their integration of networking and computing with physical systems. Prior work in our group has developed detection strategies for nonlinear systems under sensor, actuator, and combined sensor and actuator attacks which can ensure, under characterizable conditions, that attacks can be detected before they cause safety issues. However, this work did not take into account the potential that an attacker could attempt to provide data to a process that causes an attack to remain undetected but that also is consistent with different process dynamics than those which the process has. This could be problematic if process models are consistently updated online based on the most recent process data, such that skewed data during routine process operation (or during operations designed to produce informative data for model updates) could lead to new models being developed for model-based controllers that are, in a sense, “specified” by the attacker. This work provides steps toward understanding how to prevent an attacker from achieving these types of data poisoning attacks for processes under Lyapunov-based economic model predictive control (LEMPC).

Disciplines

Controls and Control Theory | Information Security | Process Control and Systems

Comments

© American Automatic Control Council (AACC) 2025. Peer Reviewed Conference Proceeding, 2025 American Control Conference (ACC), July 8-10, 2025, Denver, CO, USA. Originally published at https://doi.org/10.23919/ACC63710.2025.11107455. Financial support from the National Science Foundation CNS-1932026 and Wayne State University is gratefully acknowledged.

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