Heuristic Synthesis of Covert Attackers Against Unknown Supervisors
Liyong Lin, Ruochen Tai, Yuting Zhu, Rong Su · 2021 60th IEEE Conference on Decision and Control (CDC) · 2021
Recently, the problem of model-based synthesis of covert attackers has received a lot of attention in the discrete event systems literature. However, all the existing works assume the model of the supervisor to be available (to the adversary) for the synthesis to be effective, which can be unrealistic in practice. In this work, we consider a much more challenging, but more practical, setup where the model of the supervisor is in general not available. To compensate this lack of knowledge, we assume that the adversary has recorded a (prefix-closed) finite set of observations of the runs of the closed-loop system, which can be used for assisting the synthesis. We present a heuristic algorithm for the synthesis of covert damage-reachable attackers, based on the model of the plant and the (finite) set of observations, by a transformation into solving an instance of the partial-observation supervisor synthesis problem over certain surrogate plant. Due to the over-approximation involved in the surrogate plant, the heuristic is provably sound, but in general it is not complete. For simplicity, in this paper we only consider covert attackers that are able to carry out sensor replacement attacks and actuator disablement attacks.