This article focuses on the stealthy attack design for cyber-physical systems under Kullback–Leibler divergence, where the attacker's objective is to maximize the remote error covariances while maintaining undetected. A novel historical data-based attack model with only two attack parameters is proposed. Within the framework, the attack parameters are solved analytically, which results in a better attack performance and a significant parameter reduction compared to the existing attack strategies. Finally, simulation and experiment results are given to demonstrate the proposed strategy.

This article designs the stealthy attack against industrial cyber-physical systems under the random access protocol (RAP), which is used to avoid data collisions. The aim is to design an optimal attack that degrades the estimation performance while remaining strictly stealthy. Under the RAP, only a part of the transmitted data can be utilized to construct attack signals at each time instant, which brings challenges to attack design. To obtain the precondition for attack design, a novel modified algebraic Riccati equation (MARE) is derived, where the MARE’s convergence problem is analyzed and a sufficient condition for the existence of the modified Kalman filter’s steady state is presented. Then, the optimal attack strategy is obtained by solving a convex optimization problem. Furthermore, the analytical optimal attack parameters are presented and proved. Finally, experiments and simulations are performed to verify our results.