Exploring Reinforcement Learning for Adaptive Key Refresh in IoT Networks

Within the Key management process in IoT networks, the key refresh step is essential and unavoidable to defend against node compromise attacks. Periodic key refresh ensures the self-healing capability of key management solutions. However, the main challenge is to strike a balance between energy efficiency and effective threat mitigation when determining when to trigger key updates. To address this trade-off, we propose a Reinforcement Learning (RL)-based approach that dynamically adjusts refresh intervals based on real-time energy consumption and attack severity. Our method initiates key updates only when necessary, enabling efficient self-healing. Experimental results across various scenarios demonstrate that our solution significantly improves energy efficiency while preserving self-healing property, highlighting its potential for adaptive key refresh in IoT environments.