Enhancing Security in Industrial IoT: A Taxonomy-driven Approach to Risk Assessment

The Industrial Internet of Things (IIoT) embodies the emerging fourth revolution, which strongly focuses on Machine-to-Machine (M2M) communications, big data, and predictive analytics. One of the major challenges associated with this deployment is physical and cyber security, as new emerging devices and technology have paved the way for new threat vectors, and current security measures have a limited endpoint focus and are inadequate to accommodate the broad scale of these emerged complex and distributed IIoT systems. To create secure and safe IoT systems, a comprehensive risk assessment that can span the entire physical and cyber stack of IIoT systems is needed. In this article, we investigate the current risk assessment frameworks, discuss their strengths and challenges, and show that current frameworks do not sufficiently work for IIoT deployments. We subsequently present a novel security taxonomy to supplement and address existing assessment frameworks' challenges. We validate this proposed taxonomy with extensive recent research literature.