Optimal DoS Attack Energy Allocation in Cyber–Physical Systems Based on Deep Reinforcement Learning

The security of wireless cyber–physical systems (CPSs) faces significant challenges from channel congestion attacks, which disrupt data transmission and threaten system stability. Existing research on attack energy allocation primarily focuses on single-sensor, single-channel scenarios or assumes discrete energy distribution, limiting the development of optimal strategies. To address this, we model the remote state estimation problem in wireless CPSs as a Markov decision process (MDP) and propose an action mapping mechanism (AM). Based on these, we propose two reinforcement learning algorithms to address the continuous attack energy allocation problem in multisensor, multichannel environments. The first algorithm, genetic algorithm-driven model-based action mapping algorithm (GA-MB-AM), is a model-based reinforcement learning approach that uses a genetic algorithm for network initialization. The second algorithm is soft actor–critic-based action mapping algorithm (SAC-AM), which improves performance in dynamic environments, such as systems with energy harvesting. Simulations demonstrate the superiority of our approach over traditional methods.