Stochastic detection against deception attacks in CPS: Performance evaluation and game-theoretic analysis

Abstract

In this paper, we consider a security problem of remote state estimation in cyber–physical systems (CPS). The state measurements of a dynamic process, obtained by wireless sensors, may be modified deceptively by a malicious attacker, which may bypass a standard χ2 detector. On the other hand, the estimation performance with a standard χ2 detector typically is also difficult to analyze. As such, a stochastic detection mechanism with a varying triggering threshold is proposed. We provide an analytical performance evaluation for the estimation with the proposed detector, and propose a potential countermeasure to actively defend the deception attacks with the proposed detector. The interactive decision-making process between the system with the countermeasure and the attacker is then studied under a game-theoretic framework. In addition, we also extend the stochastic false data detection problem into a multi-sensor setting with a sequential data fusion process and more general attacking patterns, and analyze the estimation performance. Simulations are provided to illustrate the developed results.