This paper investigates the optimal denial-of-service (DoS) attack scheduling problem in multi-systems operating over multiple Markovian fading channels. At each time instant, each smart sensor obtains the local state estimate of its associated subsystem and transmits it to a remote estimator via its corresponding wireless communication channel. During this process, malicious DoS attackers inject interference into the channels, thereby increasing the packet-drop probability. However, because the attackers have limited energy resources, they cannot attack all transmissions simultaneously. Consequently, an attack scheduling strategy must be designed to determine which channels to target under the energy constraint. To address this issue, a Markov decision process (MDP) model is established. Moreover, the existence of an optimal stationary policy for the formulated MDP model is rigorously established, and its structural properties are analyzed. Furthermore, a reinforcement learning based DoS attack scheduling algorithm is developed to approximate the optimal policy. Finally, simulation results are provided to demonstrate the superiority of the proposed method.
Zhang et al. (Wed,) studied this question.