Optimal stochastic event-triggered attack on remote state estimation

This paper designs optimal stealthy deception attacks for Kalman filter-based remote state estimation, focusing on stochastic event-triggered attack scheduling. First, an event-based attack strategy is proposed for adversaries who can intercept innovations transmitted by smart sensors, and the optimality is achieved among all stealthy candidates using the same information set and scheduling. Additionally, the parameters of the employed scheduling are fine-tuned by solving a convex optimization problem. Furthermore, with the help of a variable separation technique for analyzing probability density functions under the event-triggered mechanism, this work designs an optimal attack strategy for attackers who can install additional sensors to measure the system states. Simulations verify the effectiveness of the proposed methods.