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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

Radanliev, Petar, De Roure, David, Page, Kevin, Nurse, Jason R. C., Montalvo, Rafael Mantilla, Santos, Omar, Maddox, La'Treall, Burnap, Peter (2020) 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. Cybersecurity, . E-ISSN 2523-3246. (doi:10.1186/s42400-020-00052-8) (KAR id:81278)

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Official URL
https://doi.org/10.1186/s42400-020-00052-8

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.

Item Type: Article
DOI/Identification number: 10.1186/s42400-020-00052-8
Uncontrolled keywords: Industry 4.0
Subjects: H Social Sciences
Q Science > QA Mathematics (inc Computing science)
T Technology > T Technology (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Kent Business School - Division > Kent Business School (do not use)
Depositing User: Jason Nurse
Date Deposited: 16 May 2020 09:51 UTC
Last Modified: 16 Feb 2021 14:13 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/81278 (The current URI for this page, for reference purposes)
Nurse, Jason R. C.: https://orcid.org/0000-0003-4118-1680
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