Constrained Attack-Resilient Estimation of Stochastic Cyber-Physical Systems

TL;DR

This paper introduces CARE, a constrained estimation algorithm for stochastic cyber-physical systems that enhances attack detection and estimation accuracy by incorporating physical constraints and operational limits.

Contribution

The paper presents a novel constrained attack-resilient estimation algorithm that improves accuracy and detection performance in cyber-physical systems under attack.

Findings

CARE provides less estimation error than unconstrained algorithms.

CARE reduces false negative rate in attack detection.

Estimation errors of CARE are practically exponentially stable in mean square.

Abstract

In this paper, a constrained attack-resilient estimation algorithm (CARE) is developed for stochastic cyber-physical systems. The proposed CARE can simultaneously estimate the compromised system states and attack signals. It has improved estimation accuracy and attack detection performance when physical constraints and operational limitations are available. In particular, CARE is designed for simultaneous input and state estimation that provides minimum-variance unbiased estimates, and these estimates are projected onto the constrained space restricted by inequality constraints subsequently. We prove that the estimation errors and their covariances from CARE are less than those from unconstrained algorithms and confirm that this property can further reduce the false negative rate in attack detection. We show that estimation errors of CARE are practically exponentially stable in mean square. Finally, an illustrative example of attacks on a vehicle is given to demonstrate the improved estimation accuracy and detection performance compared to an existing unconstrained algorithm.