The Industrial Internet of Things (IIoT): An Anomaly Identification and Countermeasure Method

Abstract

Networked devices benefit enterprises to gain far-reaching control over their industrial processes, which encourages them to conduct routine operations in a smart manner. Rapidly expanding interconnected sensor devices are eligible to aggregate, process and disseminate wide-ranging data. This paper proposed an extended anomaly discovery and response framework. We argued the prospective security anomalies to the IoT equipped industrial-floor and examined the numerous attacks that are conceivable on the modules in the Industrial Internet of Things (IIoT) architecture. IIoT service layer architecture was designed in consideration of high-volume device connectivity, management and security enforcement. Collection of geospatial service and device data aided the proposed framework to bridge the gap between anomaly identification and context-aware node behavior. Framework evaluation considered design principles such as node interpretability, decentralization, real-time data relay, modularity and required service alignment. Emulation outcomes specify that the malware discovery performance is better if the anomaly recognition model used the applied utility for the yield layer.