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

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 t...

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Bibliographic Details
Published in:Cybersecurity (Singapore) 2020-06, Vol.3 (1), p.1-21, Article 13
Main Authors: Radanliev, Petar, De Roure, David, Page, Kevin, Nurse, Jason R. C., Mantilla Montalvo, Rafael, Santos, Omar, Maddox, La’Treall, Burnap, Pete
Format: Article
Language:English
Subjects:
Artificial intelligence
Cognition
Computer Science
Decision support for information management, artificial intelligence and machine learning (AI/ML), dynamic self-adapting system, cognition engine, predictive cyber risk analytics
Edge computing
IIoT supply chain model
Industrial applications
Industry 4.0
Internet of Things
Machine learning
Mathematical analysis
New technology
Real time
Review
Risk analysis
Supply chain design
Supply chains
Transformational design roadmap
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Summary: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.
ISSN:2523-3246
2523-3246
DOI:10.1186/s42400-020-00052-8