Data-based optimal Denial-of-Service attack scheduling against robust control based on Q-learning

Attack optimization is an important issue in securing cyber-physical systems. This paper investigates how an attacker should schedule its denial-of-service attacks to degrade the robust performance of a closed-loop system. The measurements of system states are transmitted to a remote controller over a multichannel network. With limited resources, the attacker only has the capacity to jam sparse channels and to decide which channels should be attacked. Under an L2 framework, a data-based optimal attack strategy that uses Q-learning is proposed to maximize the effect on the closed-loop system. The Q-learning algorithm can adaptively learn the optimal attack using data sniffed over the wireless network without requiring a priori knowledge of system parameters. Simulation results sustain the performance of the proposed attack scenario.