Security Framework in IoT-based Systems: Integrating Blockchain, Deep Learning, Reinforcement Learning, and Game Theory

Abstract

IoT-based systems' security is the most critical to be guaranteed toward trusted and continuous operation in all aspects related to healthcare, smart grids, and industrial automation. This work represents an integrated approach that is supported through the use of blockchain technology, deep learning, reinforcement learning, and game theoretical approaches for increasing security and also attack detection. Blockchain ensures tamper-proof and decentralized communication, while deep learning models allow real-time anomaly detection. Reinforcement learning tunes the dynamism of the defense with evolving attack vectors, while game-theoretic models select the optimal resource allocation for proactive threat mitigation. This shows high detection rates and reduction of false positives. These results confirm that the proposed system is scalable, adaptive, and robust and thus fits into the complex IoT environments for deployment.