Reinforcement Learning Approach for IoT Security using CyberBattleSim: A Simulation-based Study

The rapid increase of IoT devices has led to significant security challenges due to their diverse nature and the complexity of potential cyber threats. This study addresses this problem by exploring reinforcement learning (RL) as a dynamic and adaptable solution for IoT security. The research employs RL within the CyberBattleSim simulation platform environment to develop and evaluate defensive strategies against cyber threats. To achieve this, a custom IoT network environment was designed, mimicking a smart house IoT network pattern and utilizing a Q-learning algorithm selected for its potential to optimize security responses in this simulated IoT network setting. The environmental parameters were designed to mimic realistic IoT scenarios, facilitating a thorough investigation of the RL technique’s efficacy. The results indicate that the Q-learning agent demonstrates superiority in identifying and mitigating threats, highlighting the technique’s potential to enhance IoT security. However, further validation in real-world settings is needed to make the study more accurate.

• Internet of Things security
• reinforcement learning
• Q-learning
• smart home networks
• network intrusion detection

• 9 — Industry, innovation and infrastructure

• Network Security and Intrusion Detection
• Internet of Things and AI
• IoT and Edge/Fog Computing
• Internet of Things security
• reinforcement learning
• Q-learning
• smart home networks
• network intrusion detection
• Computer Networks and Communications
• Information Systems
• Computer Science