Reinforcement Learning Based Blockchain for Enhanced Attack Detection in IOT Networks

The expansion of Internet of Things (IoT) networks has presented new challenges in securing these interconnected devices against a variety of cyber threats. In this paper, we propose an innovative technique that integrates reinforcement learning (RL) and blockchain technology to improve the detection of attacks in IoT networks. Our design takes advantage of blockchain decentralized nature to establish a secure and transparent framework for network communication and consensus. We develop an RL-based attack detection model that identifies anomalies and potential hazards using IoT device data, network traffic, and historical attack patterns. Integrating the RL model with the blockchain network enables it to make decisions based on learned policies while maintaining the immutability and integrity of the decision-making process. We describe the main components of our design, such as the blockchain infrastructure, IoT device interaction, attack detection model, integration of reinforcement learning and blockchain, network consensus, continuous learning, and monitoring mechanisms. We demonstrate the efficacy of our proposed system in detecting attacks, mitigating risks, and adapting to changing threat landscapes through simulations and experiments.