Event-Triggered Reverse Attacks on Remote State Estimation

Abstract

Security issues in cyber–physical systems have attracted increasing attention in recent years. In this article, a security problem in a remote estimation application is considered, where an attacker tries to degrade estimation performance via malicious attacks. In our scenario, a smart sensor transmits its innovation to a remote estimator with a residue-based false data detector. Instead of assuming that the attacker launches a consecutive man-in-the-middle attack, we consider the case with intermittent attacks. Reverse attack is a simple attack strategy, which enables an attacker to change signs of the innovation sequences. Therefore, owing to the abovementioned reasons, an event-triggered reverse attack strategy is proposed to degrade the system performance. First, the stealthiness of the event-triggered linear deception attack strategy is studied. Then, the evolution of the estimation error covariance is computed and the reverse attack is proven to be the optimal linear deception attack. We demonstrate that our event-triggered reverse attack is more destructive than a random reverse attack. Furthermore, a convex optimization problem is established to design event-triggered parameters. Comparisons of attack strategies are provided in a numerical example to validate the superiority of the reverse attack.