A Nonlinear Incremental Approach for Replay Attack Detection

TL;DR

This paper introduces a nonlinear watermark-based framework for detecting replay attacks in cyber-physical systems, balancing detection effectiveness with system performance through optimization and co-design.

Contribution

It develops a novel nonlinear watermark design framework and co-design approach for improved replay attack detection in nonlinear systems.

Findings

Watermark effects on attack detection and system performance are quantified.

The framework balances detection performance with control performance loss.

Numerical simulations validate the proposed methods.

Abstract

Replay attacks comprise replaying previously recorded sensor measurements and injecting malicious signals into a physical plant, causing great damage to cyber-physical systems. Replay attack detection has been widely studied for linear systems, whereas limited research has been reported for nonlinear cases. In this paper, the replay attack is studied in the context of a nonlinear plant controlled by an observer-based output feedback controller. We first analyze replay attack detection using an innovation-based detector and reveal that this detector alone may fail to detect such attacks. Consequently, we turn to a watermark-based design framework to improve the detection. In the proposed framework, the effects of the watermark on attack detection and closed-loop system performance loss are quantified by two indices, which exploit the incremental gains of nonlinear systems. To balance the detection performance and control system performance loss, an explicit optimization problem is formulated. Moreover, to achieve a better balance, we generalize the proposed watermark design framework to co-design the watermark, controller and observer. Numerical simulations are presented to validate the proposed frameworks.