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Leveraging Digital Twin Technology for Enhanced Cybersecurity in Cyber–Physical Production Systems
National University of Singapore, Singapore.
University of Skövde.
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.ORCID iD: 0000-0002-8927-0968
Leeds Trinity University, United Kingdom.
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2024 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 16, no 4, article id 134Article in journal (Refereed) Published
Abstract [en]

The convergence of cyber and physical systems through cyber–physical systems (CPSs) has been integrated into cyber–physical production systems (CPPSs), leading to a paradigm shift toward intelligent manufacturing. Despite the transformative benefits that CPPS provides, its increased connectivity exposes manufacturers to cyber-attacks through exploitable vulnerabilities. This paper presents a novel approach to CPPS security protection by leveraging digital twin (DT) technology to develop a comprehensive security model. This model enhances asset visibility and supports prioritization in mitigating vulnerable components through DT-based virtual tuning, providing quantitative assessment results for effective mitigation. Our proposed DT security model also serves as an advanced simulation environment, facilitating the evaluation of CPPS vulnerabilities across diverse attack scenarios without disrupting physical operations. The practicality and effectiveness of our approach are illustrated through its application in a human–robot collaborative assembly system, demonstrating the potential of DT technology. © 2024 by the authors.

Place, publisher, year, edition, pages
MDPI, 2024. Vol. 16, no 4, article id 134
Keywords [en]
asset visibility, cybersecurity, cyber–physical system (CPS), dependence analysis, digital twin (DT), manufacturing system, mitigation prioritization, Network security, Visibility, Cybe-physical systems, Cyber physicals, Cyber security, Cyber-physical systems, Cybe–physical system, Digital twin, Prioritization
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:bth-26191DOI: 10.3390/fi16040134ISI: 001210241000001Scopus ID: 2-s2.0-85191387617OAI: oai:DiVA.org:bth-26191DiVA, id: diva2:1858030
Part of project
Virtual factories with knowledge-driven optimization (VF-KDO), Knowledge Foundation
Funder
EU, Horizon 2020Knowledge FoundationAvailable from: 2024-05-15 Created: 2024-05-15 Last updated: 2024-05-24Bibliographically approved

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Ding, Jianguo

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