Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916530870026240 |
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| author | Zemskov, Alexander D. Fu, Yao Li, Runchao Wang, Xufei Karkaria, Vispi Tsai, Ying-Kuan Chen, Wei Zhang, Jianjing Gao, Robert Cao, Jian Loparo, Kenneth A. Li, Pan |
| author_facet | Zemskov, Alexander D. Fu, Yao Li, Runchao Wang, Xufei Karkaria, Vispi Tsai, Ying-Kuan Chen, Wei Zhang, Jianjing Gao, Robert Cao, Jian Loparo, Kenneth A. Li, Pan |
| contents | In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13939 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey Zemskov, Alexander D. Fu, Yao Li, Runchao Wang, Xufei Karkaria, Vispi Tsai, Ying-Kuan Chen, Wei Zhang, Jianjing Gao, Robert Cao, Jian Loparo, Kenneth A. Li, Pan Systems and Control In Industry 4.0, the digital twin is one of the emerging technologies, offering simulation abilities to predict, refine, and interpret conditions and operations, where it is crucial to emphasize a heightened concentration on the associated security and privacy risks. To be more specific, the adoption of digital twins in the manufacturing industry relies on integrating technologies like cyber-physical systems, the Industrial Internet of Things, virtualization, and advanced manufacturing. The interactions of these technologies give rise to numerous security and privacy vulnerabilities that remain inadequately explored. Towards that end, this paper analyzes the cybersecurity threats of digital twins for advanced manufacturing in the context of data collection, data sharing, machine learning and deep learning, and system-level security and privacy. We also provide several solutions to the threats in those four categories that can help establish more trust in digital twins. |
| title | Security and Privacy of Digital Twins for Advanced Manufacturing: A Survey |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2412.13939 |