A Bayesian game approach for node-based attribution defense against asymmetric information attacks in IoT networks - Jun Chen; Xin Sun; Wen Tian; Guangjie Liu (PLoS ONE, 2025)
In the rapidly evolving landscape of the Internet of Things (IoT), traditional defense mechanisms struggle to counter sophisticated attribution attacks, especially under asymmetric information conditions. This paper introduces a novel Bayesian game framework—the Node-Based Attribution Attack-Defense Bayesian Game (NAADBG) Model—to address these challenges in IoT networks. The model incorporates a comprehensive set of attacker and defender profiles, capturing the complexities of real-world security scenarios. We develop a refined method for quantifying the payoffs of node-level attack-defense actions and explore the existence of a Mixed Strategy Bayesian Nash Equilibrium (MSBNE), enabling optimal defense strategy selection. Our simulations demonstrate that the NAADBG model significantly enhances network defense performance by optimizing resource allocation and preempting potential threats. This approach provides critical insights into developing proactive defense strategies against attribution attacks, contributing to more resilient IoT security frameworks. The results show that this method not only improves network defense performance but also presents practical applications in strengthening real-time IoT environments.