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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
Cybersecurity Pub Date : 2020-06-02 , DOI: 10.1186/s42400-020-00052-8
Petar Radanliev , David De Roure , Kevin Page , Jason R. C. Nurse , Rafael Mantilla Montalvo , Omar Santos , La’Treall Maddox , Pete Burnap

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.

中文翻译:

边缘网络风险:工业物联网和工业 4.0 供应链中网络风险分析和人工智能的当前和未来趋势

数字技术改变了供应链运营的结构方式。在本文中,我们对新技术对供应链的影响和相关网络风险的文献进行了系统的综合。分类/分支方法用于评估工业物联网和工业 4.0 中供应链集成领域的进展,特别关注减轻网络风险。基于对新型网络风险和供应链与新技术集成相关问题的关键评估,提出了一个分析框架。本文确定了一个动态和自适应供应链系统,支持人工智能和机器学习 (AI/ML) 以及用于预测性网络风险分析的实时智能。该系统集成到一个认知引擎中,该引擎通过来自边缘物联网网络的实时情报实现预测性网络风险分析。这增强了能力并有助于全面了解部署边缘计算节点以及将 AI/ML 技术迁移到物联网网络外围时出现的机遇和威胁。
更新日期:2020-06-02
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