Digital Twin-based Intrusion Detection for Industrial Control SystemsShow others and affiliations
2022 (English)In: 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022, Institute of Electrical and Electronics Engineers Inc. , 2022, p. 611-617Conference paper, Published paper (Refereed)
Abstract [en]
Digital twins have recently gained significant interest in simulation, optimization, and predictive maintenance of Industrial Control Systems (ICS). Recent studies discuss the possibility of using digital twins for intrusion detection in industrial systems. Accordingly, this study contributes to a digital twin-based security framework for industrial control systems, extending its capabilities for simulation of attacks and defense mechanisms. Four types of process-aware attack scenarios are implemented on a standalone open-source digital twin of an industrial filling plant: command injection, network Denial of Service (DoS), calculated measurement modification, and naive measurement modification. A stacked ensemble classifier is proposed as the real-time intrusion detection, based on the offline evaluation of eight supervised machine learning algorithms. The designed stacked model outperforms previous methods in terms of F1Score and accuracy, by combining the predictions of various algorithms, while it can detect and classify intrusions in near real-time (0.1 seconds). This study also discusses the practicality and benefits of the proposed digital twin-based security framework
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2022. p. 611-617
Keywords [en]
Digital Twin, Industrial Control Systems, Intrusion Detection Systems, Machine Learning, Stacked Ensemble Model, Denial-of-service attack, E-learning, Intrusion detection, Supervised learning, Ensemble models, Industrial systems, Intrusion-Detection, Machine-learning, Predictive maintenance, Security frameworks, Simulation optimization, Learning algorithms
National Category
Gerontology, specialising in Medical and Health Sciences
Identifiers
URN: urn:nbn:se:ri:diva-59341DOI: 10.1109/PerComWorkshops53856.2022.9767492Scopus ID: 2-s2.0-85130615468ISBN: 9781665416474 (electronic)OAI: oai:DiVA.org:ri-59341DiVA, id: diva2:1673078
Conference
2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2022Pisa21 March 2022 through 25 March 2022
Note
Funding details: 101007273, 876038; Funding details: Horizon 2020 Framework Programme, H2020; Funding text 1: This work was supported by InSecTT (www.insectt.eu) and DAIS (www.dais-project.eu), which received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038 and No 101007273. The JU receives support from the European Union s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey, Belgium, Germany, Czech Republic, Denmark, Norway. The document reflects only the authors views and the Commission is not responsible for any use that may be made of the information it contains.; Funding text 2: ACKNOWLEDGMENT This work was supported by InSecTT (www.insectt.eu) and DAIS (www.dais-project.eu), which received funding from the KDT Joint Undertaking (JU) under grant agreement No 876038 and No 101007273. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Austria, Sweden, Spain, Italy, France, Portugal, Ireland, Finland, Slovenia, Poland, Netherlands, Turkey, Belgium, Germany, Czech Republic, Denmark, Norway.
2022-06-202022-06-202023-10-30Bibliographically approved