Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Digital Twin-Enhanced Methodology for Training Edge-Based Models for Cyber Security Applications.
AU - Allison, David
AU - Smith, Paul
AU - McLaughlin, Kieran
PY - 2022/12/15
Y1 - 2022/12/15
N2 - 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.
AB - 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.
KW - Cyber Security
KW - Digital Twins
KW - Edge Computing
KW - Machine Learning
U2 - 10.1109/INDIN51773.2022.9976095
DO - 10.1109/INDIN51773.2022.9976095
M3 - Conference contribution/Paper
T3 - IEEE International Conference on Industrial Informatics (INDIN)
SP - 226
EP - 232
BT - 2022 IEEE 20th International Conference on Industrial Informatics, INDIN 2022
PB - IEEE
ER -