# An Optimal Linear Dynamic Detection Method for Replay Attack in Cyber-Physical Systems

## TL;DR

This paper introduces an optimal linear dynamic detection method for replay attacks in cyber-physical systems, enhancing detection stability and minimizing control performance loss through parameter optimization.

## Contribution

It proposes a novel dynamic attack detector coupled with system dynamics, improving detection capability while maintaining system stability and optimizing performance impact.

## Key findings

- The proposed detector effectively reveals replay attacks by destabilizing residuals.
- Simulation results show the method outperforms traditional additive white noise watermarking.
- The approach allows adjustable detection sensitivity through parameter tuning.

## Abstract

The problem of detecting replay attack to the linear stochastic system with Kalman filer state estimator and LQG controller is addressed. To this end, a dynamic attack detector method is proposed which is coupled with the dynamics of the system. While preserving stability of the main system, conditions on parameters of the attack detector dynamics are obtained such that the attack can be revealed by destabilization of a residual trajectory which is the difference between the estimated and measured output of the system. Using this method, system operator can adjust the detection rate based on the proposed scheme by changing the design parameters. Nevertheless, since the exogenous attack detector signal affects the performance of the closed loop control system, we propose an optimization problem to determine such a detector with minimum loss effect. In the simulation results, the proposed dynamical attack detector approach is compared with the well-known additive white noise watermarking method and the results confirm the superiority of the new scheme.