A Genetic Algorithm and Game-Theoretic Model for DDoS Defense in IoT Networks

The rapid expansion of the Internet of Things (IoT) has introduced significant advancements in real-time monitoring and management, but it has also brought new security challenges, particularly from Distributed Denial of Service (DDoS) attacks. These attacks pose a persistent threat to IoT networks, especially impacting resource-constrained edge nodes. This paper presents a novel Genetic Algorithm and Game-based Defense (G2D) model, designed to identify and adaptively apply optimal strategies to defend against DDoS attacks. The G2D model integrates genetic algorithms and game theory to dynamically determine equilibrium strategies, where defense mechanisms such as high- and lowinteraction honeypots and rate limiting are adjusted based on the intensity of incoming attacks to optimize resource allocation. By modeling attacker-defender interactions with bounded rationality, the system continuously refines its strategies over multiple iterations, adapting to evolving attack patterns. Simulation results indicate that the G2D model offers stable and adaptive defenses, achieving higher average payoffs, and a reduced outcome variance. Additionally, the model shows robust adaptability across different attack volumes, making it a reliable solution for enhancing IoT network security.