Venue & metadata

• Journal/Proceedings: Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023
• Pages: 301 – 306
• Note: Cited by: 3
• Author keywords: Digital Twins; Process Mining; Requirement Engineering; Resilience Indices

Abstract

Nowadays, the problem of system robustness, es-pecially in critical infrastructures, is a challenging open question. Some systems provide crucial services continuously failing, threatening the availability of the provided services. By designing a robust architecture, this criticality could be overcome or limited, ensuring service continuity. The definition of a resilient system involves not only its architecture but also the methodology implemented for the calculation and analysis of some indices, quantifying system performance. This study provides an innovative architecture for Digital Twins implementation based on a hybrid methodology for improving the control system in realtime. The introduced approach brings together different techniques. In particular, the work combines the point of strengths of Model-based methods and Data-driven ones, aiming to improve system performances. © 2023 IEEE.

Keywords

Criticality (nuclear fission) GS Data-driven approach GS Model-based OPC GS Modeling data GS Process mining GS Requirement engineering GS Resilience index GS Resilient systems GS System robustness GS Systems performance GS Twin design GS Computer architecture GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Campanile, L., De Biase, M. S., De Fazio, R., Di Giovanni, M., Marulli, F., & Verde, L. (2023). Merging Model-Based and Data-Driven Approaches for Resilient Systems Digital Twins Design [Conference paper]. Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023, 301–306. https://doi.org/10.1109/CSR57506.2023.10224945