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A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems

Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suff...

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Detalles Bibliográficos
Autores principales: Tang, Bin, Lu, Yan, Li, Qi, Bai, Yueying, Yu, Jie, Yu, Xu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920089/
https://www.ncbi.nlm.nih.gov/pubmed/36772180
http://dx.doi.org/10.3390/s23031141
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author Tang, Bin
Lu, Yan
Li, Qi
Bai, Yueying
Yu, Jie
Yu, Xu
author_facet Tang, Bin
Lu, Yan
Li, Qi
Bai, Yueying
Yu, Jie
Yu, Xu
author_sort Tang, Bin
collection PubMed
description Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets.
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spelling pubmed-99200892023-02-12 A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems Tang, Bin Lu, Yan Li, Qi Bai, Yueying Yu, Jie Yu, Xu Sensors (Basel) Article Industrial Cyber-Physical Systems (ICPS) connect intelligent manufacturing equipment equipped with sensors, wireless and RFID communication technologies through data interaction, which makes the interior of the factory, even between factories, become a whole. However, intelligent factories will suffer information leakage and equipment damage when being attacked by ICPS intrusion. Therefore, the network security of ICPS cannot be ignored, and researchers have conducted in-depth research on network intrusion detection for ICPS. Though machine learning and deep learning methods are often used for network intrusion detection, the problem of data imbalance can cause the model to pay attention to the misclassification cost of the prevalent class, but ignore that of the rare class, which seriously affects the classification performance of network intrusion detection models. Considering the powerful generative power of the diffusion model, we propose an ICPS Intrusion Detection system based on the Diffusion model (IDD). Firstly, data corresponding to the rare class is generated by the diffusion model, which makes the training dataset of different classes balanced. Then, the improved BiLSTM classification network is trained on the balanced training set. Extensive experiments are conducted to show that the IDD method outperforms the existing baseline method on several available datasets. MDPI 2023-01-19 /pmc/articles/PMC9920089/ /pubmed/36772180 http://dx.doi.org/10.3390/s23031141 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tang, Bin
Lu, Yan
Li, Qi
Bai, Yueying
Yu, Jie
Yu, Xu
A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title_full A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title_fullStr A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title_full_unstemmed A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title_short A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems
title_sort diffusion model based on network intrusion detection method for industrial cyber-physical systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9920089/
https://www.ncbi.nlm.nih.gov/pubmed/36772180
http://dx.doi.org/10.3390/s23031141
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