Digital Twin-Enhanced Methodology for Training Edge-Based Models for Cyber Security Applications.

Allison, David and Smith, Paul and McLaughlin, Kieran (2022) Digital Twin-Enhanced Methodology for Training Edge-Based Models for Cyber Security Applications. In: 2022 IEEE 20th International Conference on Industrial Informatics, INDIN 2022 :. IEEE International Conference on Industrial Informatics (INDIN) . IEEE, pp. 226-232. ISBN 9781728175683

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Abstract

Digital twins can address the problem of data scarcity during the training machine learning models, as they can be used to simulate and explore a range of process conditions and system states that are too difficult or dangerous to explore in real-world Cyber-Physical Systems (CPSs). Meanwhile, advances in industrial control systems technology have enabled increasingly complex functionality to be deployed on or near so-called edge devices, such as Programmable Logic Controllers (PLCs).In this paper, we propose a methodology for training a machine learning model offline using data extracted from a digital twin, before converting the model for deployment on an edge device to perform anomaly detection. To examine the model's suitability for anomaly detection, we execute several simulations of fault conditions. Results show that the model can successfully predict normal operations as well as identify faults and cyber-attacks. There is a negligible drop in performance on the edge device, when compared to executing the model on a personal computer, but it remains suitable for the application.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
Research Output Funding/no_not_funded
Subjects:
?? cyber securitydigital twinsedge computingmachine learningno - not fundednoinformation systemscomputer science applications ??
ID Code:
219118
Deposited By:
Deposited On:
23 May 2024 11:30
Refereed?:
Yes
Published?:
Published
Last Modified:
23 May 2024 11:30