Insider threat detection within operational technology using digital twins.

Petrovski, Andrei; Kotenko, Igor; Arifeen, Murshedul; Abramenko, Georgy; Sobolev, Pavel

Abstract

Managing unintentional insider threat is a growing challenge in digital industries because the biggest threat to operational technologies (OT) originates internally, irrespective of the type or size of the organisation. Data breaches and other advanced persistent threats are often caused by users with legitimate access to systems who often make genuine mistakes. This paper highlights the necessity to bring forward a more proactive approach in terms of understanding, raising awareness, and tackling unintentional insider threats. A novel effective method of securing OT systems against insider threats based on data-driven modelling and machine learning has been suggested, tested and trialled using a digital twin that provides a secure and conducive environment for addressing operational challenges in the era of Industry 4.0/5.0.

Citation

PETROVSKI, A., KOTENKO, I., ARIFEEN, M., ABRAMENKO, G. and SOBOLEV, P. 2024. Insider threat detection within operational technology using digital twins. In Kovalev, S., Kotenko, I., Sukhanov, A., Li, Y. and Li Y. (eds.) Proceedings of the 8th Intelligent information technologies for industry international scientific conference (IITI'24), 1-7 November 2024, Harbin, China. Lecture notes in networks and systems, 1210. Cham: Springer [online], volume 2, pages 25-34. Available from: https://doi.org/10.1007/978-3-031-77411-9_3

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 8th Intelligent information technologies for industry international scientific conference (IITI'24)
• Start Date: Nov 1, 2024
• End Date: Nov 7, 2024
• Acceptance Date: Jun 24, 2024
• Online Publication Date: Dec 19, 2024
• Publication Date: Dec 20, 2024
• Deposit Date: Apr 17, 2025
• Publicly Available Date: Dec 20, 2025
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 2
• Pages: 25-34
• Series Title: Lecture notes in networks and systems
• Series Number: 1210
• Series ISSN: 2367-3370; 2367-3389
• Chapter Number: Harbin, China
• ISBN: 9783031774102
• DOI: https://doi.org/10.1007/978-3-031-77411-9_3
• Keywords: Insider threats; Threat detection; Systems security
• Public URL: https://rgu-repository.worktribe.com/output/2626118