AI-driven dynamic trust management and blockchain-based security in industrial IoT

Abstract

The Industrial Internet of Things (IIoT) revolutionizes industrial operations through real-time data exchange and analytics but introduces significant security and trust challenges particularly in dynamic and distributed IIoT environments. We hypothesize that integrating an AI-driven Trust Management System (TMS) with blockchain technology can address these issues effectively. This paper proposes a framework combining an AI-driven Dynamic TMS (AI-DTMS) with a private blockchain. AI-DTMS evaluates the reliability of the device and data using machine learning, achieving 96.31% accuracy with minimal false positives. The blockchain module ensures secure authentication, achieving nearly 100% success. It mitigates critical threats, including spoofing, Sybil, node-capturing, replay, and DDoS attacks, ensuring robust security in IIoT environments. Performance evaluations demonstrate 35% improvement in response time and up to 97.8% reduction in latency, underscoring scalability and efficiency. By integrating AI-DTMS with blockchain, the framework enhances trust, security, and performance in dynamic IIoT environments, offering a scalable and robust solution.