Attack Detection for Cyber-Physical Systems Using Data-Driven Maximum Likelihood Estimation

This study examines data-driven attack detectors that utilize maximum likelihood estimation for cyber-physical systems. The proposed methodology optimally estimates the input-output trajectories of these systems using both pre-experimental data and real-time measurements, in accordance with the principles of maximum likelihood. The attack detector identifies the presence of an attack by comparing the estimated trajectories with actual measured trajectories. The theoretical contributions of this study include the demonstration of a fundamental limitation, specifically, the set of undetectable attacks when disturbances are negligible. Furthermore, when disturbances can not be disregarded, the proposed method can detect attacks with a specified false rate based on the detectable condition derived through a χ2 test.