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A digital twin based industrial automation and control system security architecture
Lund University, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science. Lund University, Sweden.
2020 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 16, no 1, p. 669-680, article id 8822494Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE Computer Society , 2020. Vol. 16, no 1, p. 669-680, article id 8822494
Keywords [en]
Digital twin, security, security analysis, security framework, state replication, Automation, Security frameworks, State replications, Electronic document exchange
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-44179DOI: 10.1109/TII.2019.2938885Scopus ID: 2-s2.0-85078224349OAI: oai:DiVA.org:ri-44179DiVA, id: diva2:1396881
Note

Funding details: Stiftelsen för Strategisk Forskning, SSF; Funding details: 768892; Funding text 1: Manuscript received April 9, 2019; revised June 17, 2019 and July 23, 2019; accepted August 14, 2019. Date of publication September 2, 2019; date of current version January 4, 2020. This work was supported in part by the Framework Grant RIT17-0032 from the Swedish Foundation for Strategic Research and in part by the EU H2020 Project CloudiFacturing under Grant 768892. Paper no. TII-19-1326. (Corresponding author: Christian Gehrmann.) C. Gehrmann is with the Department of Electrical and Information Technology, Lund University, 22100 Lund, Sweden (e-mail:, christian.gehrmann@eit.lth.se).

Available from: 2020-02-26 Created: 2020-02-26 Last updated: 2020-02-26Bibliographically approved

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