The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
A Digital Twin Based Industrial Automation and Control System Security Architecture
Open accessAbstract
The digital twin is a rather new industrial control and automation systems concept. While the approach so far has gained interest mainly due to capabilities to make advanced simulations and optimizations, recently the possibilities for enhanced security have got attention within the research community. In this article, we discuss how a digital twin replication model and corresponding security architecture can be used to allow data sharing and control of security-critical processes. We identify design-driving security requirements for digital twin based data sharing and control. We show that the proposed state synchronization design meets the expected digital twin synchronization requirements and give a high-level design and evaluation of other security components of the architecture. We also make performance evaluations of a proof of concept for protected software upgrade using the proposed digital twin design. Our new security framework provides a foundation for future research work in this promising new area.
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Citations
Cites
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Spectre Attacks: Exploiting Speculative Execution
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About The Importance of Autonomy and Digital Twins for the Future of Manufacturing
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BackgroundThe OAuth 2.0 Authorization Framework
(2012) · doi:10.17487/rfc6749 · Cited by 1,383
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“The architecture adopts best practice for factory network isolation [5] to meet R6.”
BackgroundCyber-Physical Systems Security—A Survey
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Cited by
Digital Twins: A Survey on Enabling Technologies, Challenges, Trends and Future Prospects
Mihai, S., Yaqoob, M., Hung, D. V., Davis, W., Towakel, P., Raza, M., Karamanoglu, M., Barn, B., Shetve, D., Prasad, R. V., et al. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3208773 · Cited by 1,049
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Chengoden, R., Victor, N., Huynh-The, T., Yenduri, G., Jhaveri, R. H., Alazab, M., Bhattacharya, S., Hegde, P., Maddikunta, P. K. R., & Gadekallu, T. R. (2023) · IEEE Access · doi:10.1109/access.2023.3241628 · Cited by 508
Reducing Offloading Latency for Digital Twin Edge Networks in 6G
Sun, W., Zhang, H., Wang, R., & Zhang, Y. (2020) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2020.3018817 · Cited by 373
Wang, Y., Su, Z., Guo, S., Dai, M., Luan, T. H., & Liu, Y. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3263909 · Cited by 357
Zhou, X., Xu, X., Liang, W., Zeng, Z., Shimizu, S., Yang, L. T., & Jin, Q. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3061419 · Cited by 308
Metadata
- Authors
- Gehrmann, C., & Gunnarsson, M.
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-01
- Volume
- 16
- Issue
- 1
- Pages
- 669-680
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2972160586
- Cited by
- 353
- References
- 7
- FWCI
- 21.19
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
