Document Type

Article

Abstract

Cyberattacks on control systems can create unprofitable and unsafe operating conditions. To enhance safety and attack resiliency of control systems, cyberattack detection strategies can be developed. Prior work in our group has sought to develop cyberattack detection strategies that are integrated with an advanced control formulation known as Lyapunov-based economic model predictive control (LEMPC), in the sense that the controller properties can be used to analyze closed-loop stability in the presence or absence of undetected attacks. In this work, we consider neural network-approximated control laws, concepts for mitigating cyberattacks on such control laws, and how these ideas elucidate concepts in how to fight back against cyberattacks. We begin by providing sufficient conditions under which a neural network (NN) that approximates an LEMPC maintains safety for a sampling period after a cyberattack by inheriting safety properties from the LEMPC formulation. Then, we discuss a second concept inspired by neural network repair in the presence of adversarial attacks for attempting to ensure safety of controllers for a time period after undetected attacks, even those not based on a rigorous control law formulation like LEMPC. We examine the potential conservatism differences between the LEMPC-based safety strategy and one based on repairing problematic control actions, and discuss how this concept can inspire ideas for fighting back against attacks.

Disciplines

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

Comments

© 2024 The Authors. Peer Reviewed Conference Proceeding, 12th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM 2024), Toronto, Canada, July 14-17, 2024. Distributed under a Creative Commons Attribution NonCommercial NoDerivatives 4.0 License (CC-BY-NC-ND, https://creativecommons.org/licenses/by-nc-nd/4.0/). Originally published at https://doi.org/10.1016/j.ifacol.2024.08.399

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