Digital twin-enhanced methodology for training edge-based models for cyber security applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publication2022 IEEE 20th International Conference on Industrial Informatics (INDIN): proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages226-232
Number of pages7
ISBN (Electronic)9781728175683
ISBN (Print)9781728175690
DOIs
Publication statusPublished - 15 Dec 2022
Event20th IEEE International Conference on Industrial Informatics, INDIN 2022 - Perth, Australia
Duration: 25 Jul 202228 Jul 2022

Publication series

NameIEEE International Conference on Industrial Informatics (INDIN): proceedings
Volume2022-July
ISSN (Print)1935-4576
ISSN (Electronic)2378-363X

Conference

Conference20th IEEE International Conference on Industrial Informatics, INDIN 2022
Country/TerritoryAustralia
CityPerth
Period25/07/202228/07/2022

Bibliographical note

Funding Information:
The research presented in this paper was supported by the ERANet-funded project Resili8 (Austrian FFG Contract No. 895526).

Publisher Copyright:
© 2022 IEEE.

Keywords

  • Cyber Security
  • Digital Twins
  • Edge Computing
  • Machine Learning

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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