Model-Based Reinforcement Learning for Containing Malware Propagation in Wireless Radar Sensor Networks
Haitao Lin, Can Tian, Linman Chen, Daizhi Liao, Yunbo Wang, Yubo Hua · Actuators · 2025
To address malware containment challenges in WRSNs—where traditional integer-order models neglect propagation memory effects and standard reinforcement learning (RL) suffers from slow trial-and-error limitations—we propose the following: (1) a fractional-order VCISQ epidemic model capturing temporal dependencies for higher accuracy, and (2) a model-based Soft Actor–Critic (MBSAC) method, which integrates a learned transition model into an actor–critic architecture to predict future states from limited data, accelerating learning. Experiments confirm MBSAC outperforms RL baselines by reducing control overhead, hastening convergence, and enhancing robustness. It alleviates the rigidity of the traditional method and establishes a reward-driven safeguard for WRSNs.