Abstract
Industrial Internet-of-Things (IIoT) is a powerful IoT application, which remodels the growth of industries by ensuring transparent communication among various entities such as hubs, manufacturing places, and packaging units. Introducing data science techniques within the IIoT improves the ability to analyze the collected data in a more efficient manner, which current IIoT architectures lack due to their distributed nature. From a security perspective, network anomalies/attackers pose high security risk in IIoT. In this article, we have addressed this problem, where a coordinator IoT device is elected to compute the trust of IoT devices to prevent the MD to be part of network. Further, the transparency of the data is ensured by integrating a blockchain-based data model. The performance of the proposed framework is validated extensively and rigorously via MATLAB against various security metrics such as attack strength, message alteration, and probability of false authentication. The simulation results suggest that the proposed solution increases IIoT network security by efficiently detecting malicious attacks in the network.