  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 paper, we have addressed this problem, where a coordinator IoT device is
elected to compute the trust of IoT devices to prevent the malicious devices 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.
