Abstract:
When the cyber-physical system (Cyber-Physical System, CPS) performs remote state estimation, it is easy for an attacker to attack the system by tampering with wirelessly transmitted measurement data, etc., thereby causing loss of system performance. In order to defend against attacks, we need to fully understand the attacker's attack strategy. According to the attacker's understanding of the system knowledge, we divide the research into two situations: one is that the attacker has limited ability and cannot directly obtain the transmission data, but can use additional sensors to get a measurement; the other one is that the attacker has a good understanding of the system, and can either directly obtain the transmission data or use its own additional sensors to measure the data. We analyze estimation performance of the attacker's optimal linear deception attack strategy under the KL divergence detector for these two situations, and transform the optimal attack problem into a convex optimization problem. Finally, we give a closed-form expression of the optimal linear deception attack in a one dimensional situation. We compare the estimation error covariance caused by the optimal attack in the two cases, and conclude that the more the attacker understands the system knowledge and the greater the impact of the attack on system performance. At the same time, it is also compared with the existing literature in terms of estimation performance and optimal attack, and uses numerical simulation to verify the effectiveness of the proposed results.