Distributed Resilient State Estimation and Control with Strategically Implemented Security Measures

TL;DR

This paper develops a distributed resilient state estimation and control framework for linear systems under malicious sensor attacks, optimizing security measures to maximize resilience while balancing costs, and validates it through vehicle platooning simulations.

Contribution

It introduces an algorithm to identify optimal security measures for resilience against sensor attacks, including conditions for efficient computation and a distributed control scheme.

Findings

Optimal security measures prevent undetectable attacks.

The proposed scheme guarantees bounded estimation and control errors.

Validated effectiveness through vehicle platooning simulations.

Abstract

This paper addresses the problem of distributed resilient state estimation and control for linear time-invariant systems in the presence of malicious false data injection sensor attacks and bounded noise. We consider a system operator (defender) capable of deploying cybersecurity measures to counteract the sensor compromises. Although such measures enhance resilience against adversarial attacks, they may incur substantial costs; hence, it is crucial to select countermeasures to balance resilience gains and cost efficiency strategically. We first demonstrate that the system's resilience against attacks is maximized through the appropriate implementation of security measures, implying that no attacker can execute undetectable sensor attacks. Building on this analysis, we propose an algorithm that identifies the optimal security measure. While determining this measure is NP-hard in general, we also derive sufficient conditions under which efficient computation is feasible. Furthermore, we develop a distributed resilient state estimation and control scheme informed by the optimal security measure and establish conditions that guarantee bounded estimation and control errors. Finally, we validate the efficacy of our approach via numerical simulations of a vehicle platooning scenario.