Optimal SINR-Based DoS Attack Scheduling for Remote State Estimation via Adaptive Dynamic Programming Approach
Ruirui Liu, Fei Hao, Hao Yu
Abstract
This article considers the energy-limited denial-of-service attack scheduling problem on remote state estimation under signal-to-interference-plus-noise ratio-based channels. The goal of the attacker is to design the optimal attack strategy to degrade the control performance of cyber-physical systems and to reduce his energy consumption. First, to weigh the importance between the current and future rewards, an optimization problem with a discount factor is formulated, which is used to reflect the attacker’s goal. Next, a Markov decision problem (MDP) is formulated to solve the optimization problem. Due to the difficulty of solving the high-dimensional MDP with unknown transition and reward functions, a value iteration adaptive dynamic programming method is proposed to achieve an approximate optimal solution. Also, convergence analysis of the proposed algorithm is carried out. Finally, simulation results are presented to show the efficiency and feasibility of the obtained results.