RMAAC: Joint Markov Games and Robust Multiagent Actor-Critic for Explainable Malware Defense in Social IoT

The end-edge-cloud-based Social Internet of Things (SIoT) faces increasing threats from malware. To address these challenges, we propose an explainable novel malware defense framework that integrates Markov games with multi-agent deep reinforcement learning under an end-edge-cloud-based SIoT collaborative architecture. The framework models the interactions between malicious SIoT nodes and edge devices as a multi-agent game problem, incorporating multi-layer defense mechanisms to achieve precise descriptions of attack-defense behaviors. By combining Markov games with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, we develop the Robust Multiagent Actor-Critic (RMAAC) algorithm, which enables adaptive strategy optimization. To enhance system interpretability, we introduce SHapley Additive exPlanations (SHAP) value analysis into the defense decision-making process, providing transparent insights into feature contributions and decision rationales. Extensive experimental results demonstrate that the proposed RMAAC algorithm significantly outperforms existing methods, including MADDPG and Minimax Multi-Agent Deep Deterministic Policy Gradient (M3DDPG), in multiple performance metrics such as episode reward, hacking success rate, and cumulative defense number. Through systematic parameter optimization, including batch size and agent interaction speed, the framework provides an effective and sustainable solution for explainable malware defense in SIoT environments.