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Lookup NU author(s): Yizhou Shen
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© 2025 Elsevier B.V.The edge intelligence (EI)-enabled social Internet of Things (SIoT) is increasingly vulnerable to sophisticated malware that exploits social relationships between devices to propagate rapidly and bypass traditional security. To address this dynamic threat under incomplete information, we propose a novel moving target defense framework based on a Bayesian Markov game. In our framework, defenders dynamically shift system configurations and resource allocations upon detecting potential threats. Based on their belief states about attacker types, each defender can decide whether to coordinate defense strategies with other agents. Unlike most existing work, we explicitly account for both the incomplete information about attacker capabilities and the dynamic nature of EI-enabled SIoT systems. We formulate a joint optimization problem to simultaneously determine belief updates about attacker types via Bayesian inference, dynamic reconfiguration of defense parameters, and optimal coordination strategies among agents. To efficiently solve this problem, we develop a novel explainable bayesian multi-agent deep deterministic policy gradient algorithm, which integrates centralized training with decentralized execution. Furthermore, we incorporate Shapley Additive Explanations to analyze agent contributions. Theoretical analyses and extensive simulations demonstrate that our proposed solution significantly outperforms traditional reinforcement learning algorithms.
Author(s): Hong T, Shen Y, Wu X, Dong J, Shen S, Liu Z
Publication type: Article
Publication status: Published
Journal: Information Fusion
Year: 2026
Volume: 130
Print publication date: 01/06/2026
Online publication date: 27/12/2025
Acceptance date: 23/12/2025
ISSN (print): 1566-2535
ISSN (electronic): 1872-6305
Publisher: Elsevier B.V.
URL: https://doi.org/10.1016/j.inffus.2025.104101
DOI: 10.1016/j.inffus.2025.104101
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