Working Paper Article Version 2 This version is not peer-reviewed

Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains

Version 1 : Received: 7 March 2019 / Approved: 11 March 2019 / Online: 11 March 2019 (09:03:42 CET)
Version 2 : Received: 24 December 2020 / Approved: 24 December 2020 / Online: 24 December 2020 (13:37:35 CET)

How to cite: Radanliev, P.; De Roure, D.; Page, K.; Nurse, J.R.; Mantilla Montalvo, R.; Santos, O.; Maddox, L.; Burnap, P. Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains. Preprints 2019, 2019030123 Radanliev, P.; De Roure, D.; Page, K.; Nurse, J.R.; Mantilla Montalvo, R.; Santos, O.; Maddox, L.; Burnap, P. Cyber Risk at the Edge: Current and future trends on Cyber Risk Analytics and Artificial Intelligence in the Industrial Internet of Things and Industry 4.0 Supply Chains. Preprints 2019, 2019030123

Abstract

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

Subject Areas

Industry 4.0; Supply Chain Design; Transformational Design Roadmap; IIoT Supply Chain Model; Decision Support for Information Management

Comments (1)

Comment 1
Received: 24 December 2020
Commenter: Petar Radanliev
Commenter's Conflict of Interests: Author
Comment: Revised text, references and title.
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