A Moving Target Defense to Detect Stealthy Attacks in Cyber-Physical Systems

Cyber-Physical Systems (CPS) have traditionally been considered more static, with regular communication patterns when compared to classical information technology networks. Because the structure of most CPS remains unchanged during long periods of time, they become vulnerable to adversaries who can tailor their attacks based on their precise knowledge of the system dynamics, communications, and control. Moving Target Defense (MTD) has emerged as a strategy to add uncertainty about the state and execution of a system in order to prevent adversaries from having predictable effects with their attacks. In this work we propose a novel type of MTD strategy that randomly changes the availability of the sensor data, so that it is harder for adversaries to tailor stealthy attacks and at the same time it can minimize the impact of false-data injection attacks. Using tools from switched control systems we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks.