https://www.mdu.se/

This study is based on a Moving Target Defence (MTD) algorithm designed to introduce uncertainty into the controller and another layer of uncertainty to intrusion detection. This randomness complicates the adversary's attempts to craft stealthy attacks while concurrently minimizing the impact of false-data injection attacks. Leveraging concepts from state observer design, the method establishes an optimization framework to determine the parameters of the random signals. These signals are strategically tuned to increase the detectability of stealthy attacks while reducing the deviation resulting from false data injection attempts. We propose here to use two different state observers and two associated MTD algorithms. The first one optimizes the parameters of the random signals to reduce the deviation resulting from false data injection attempts and maintain the stability of the closed-loop system with the desired level of performance. In contrast, the second one optimizes the parameters of the random signals to increase the detectability of stealthy attacks. Dividing the optimization problem into two separate optimization processes simplifies the search process and makes it possible to have higher values of the detection cost function. To illustrate the effectiveness of our approach, we present a case study involving a generic linear time-invariant system and compare the results with a recently published algorithm.