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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/3/1141 |
| _version_ | 1797623264058540032 |
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| author | Bin Tang Yan Lu Qi Li Yueying Bai Jie Yu Xu Yu |
| author_facet | Bin Tang Yan Lu Qi Li Yueying Bai Jie Yu Xu Yu |
| author_sort | Bin Tang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T09:26:12Z |
| format | Article |
| id | doaj.art-85837c2c6d4641efbadceeb3e866878c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T09:26:12Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-85837c2c6d4641efbadceeb3e866878c2023-11-16T17:56:44ZengMDPI AGSensors1424-82202023-01-01233114110.3390/s23031141A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical SystemsBin Tang0Yan Lu1Qi Li2Yueying Bai3Jie Yu4Xu Yu5Qingdao Innovation and Development Base, Harbin Engineering University, Qingdao 266000, ChinaCollege of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, ChinaCollege of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266000, ChinaIndustrial 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.https://www.mdpi.com/1424-8220/23/3/1141diffusion modelintrusion detectionICPSimbalanced dataBiLSTM |
| spellingShingle | Bin Tang Yan Lu Qi Li Yueying Bai Jie Yu Xu Yu A Diffusion Model Based on Network Intrusion Detection Method for Industrial Cyber-Physical Systems Sensors diffusion model intrusion detection ICPS imbalanced data BiLSTM |
| 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 | diffusion model intrusion detection ICPS imbalanced data BiLSTM |
| url | https://www.mdpi.com/1424-8220/23/3/1141 |
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