Enhancing IoT Security in SDN Environments With Game Theory and Reinforcement Learning-Driven Dynamic Resource Allocation

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Abstract
The Internet of Things (IoT) is expanding rapidly as more consumer devices become connected. This expansion presents significant security challenges for Software-Defined Networking (SDN), where static defense mechanisms are often insufficient. The proposed work presents a novel framework that enhances Intrusion Detection Systems (IDS) by combining dynamic resource allocation with game theory and Reinforcement Learning (RL). In this framework, the strategic interactions among the IDS, potential attackers, and the SDN controller are modeled to reflect the complex dynamics of consumer-centric IoT environments. The controller allocates resources in real time, adapting to network demands while maintaining security and performance. RL optimizes these decisions by enabling continuous adaptation to evolving attack patterns. Simulation results validate the effectiveness of the proposed approach, achieving a 98.2% attack detection rate with only 1.8% false positives, along with measurable improvements in throughput and latency compared to baseline methods. The proposed work resolves the key trade-off between security and efficiency, delivering a robust and efficient security framework for IoT-enabled SDN in consumer environments.

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