Optimal Stealthy Attack With Side Information Under the Energy Constraint on Remote State Estimation

This work considers the innovation-based attacks with side information under the energy constraint in cyber-physical systems where Kullback-Leibler (K-L) divergence is used as the stealthiness metric. Moreover, the attacker requires to decide when to launch attacks over a finite time horizon since the energy limitation. To cause the largest degradation to the estimation performance, the attack strategy and schedule require to be designed synergistically under the constraints. The terminal error (TE) and average error (AE) are respectively taken as attack performance indices. Then, the optimal attack policies for the TE and AE are obtained by solving a constrained optimization problem and a 0–1 programming problem. Finally, simulation examples are employed to demonstrate the results.