Intrusion Detection for Industrial Control Systems Based on Improved Contrastive Learning SimCLR

被引:1
|
作者
Li, Chengcheng [1 ,2 ,3 ,4 ]
Li, Fei [5 ]
Zhang, Liyan [6 ]
Yang, Aimin [1 ,2 ,3 ,4 ,7 ]
Hu, Zhibin [1 ,2 ,3 ,4 ]
He, Ming [1 ,2 ,3 ,7 ]
机构
[1] North China Univ Sci & Technol, Hebei Key Lab Data Sci & Applicat, Tangshan 063210, Peoples R China
[2] Key Lab Engn Comp Tangshan City, Tangshan 063210, Peoples R China
[3] North China Univ Sci & Technol, Coll Sci, Tangshan 063210, Peoples R China
[4] North China Univ Sci & Technol, Hebei Engn Res Ctr Intelligentizat Iron Ore Optimi, Tangshan 063210, Peoples R China
[5] Shanxi Jianlong Ind Co Ltd, Yuncheng 044000, Peoples R China
[6] Yanshan Univ, Coll Informat Sci & Engn, Qinhuangdao 066004, Peoples R China
[7] North China Univ Sci & Technol, Tangshan Intelligent Ind & Image Proc Technol Inno, Tangshan 063210, Peoples R China
来源
APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 16期
关键词
contrast learning; residual networks; industrial control systems; intrusion detection; RECOGNITION;
D O I
10.3390/app13169227
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Since supervised learning intrusion detection models rely on manually labeled data, the process often requires a lot of time and effort. To make full use of unlabeled network traffic data and improve intrusion detection, this paper proposes an intrusion detection method for industrial control systems based on improved comparative learning SimCLR. Firstly, a feature extraction network is trained on SimCLR using unlabeled data; a linear classification layer is added to the trained feature extraction network model; and a small amount of labeled data is used for supervised training and fine-tuning of the model parameters. The trained model is simulated on the Secure Water Treatment (SWaT) dataset and the publicly available industrial control dataset from Mississippi State University, and the results show that the method has better results in all evaluation metrics compared with the deep learning algorithm using supervised learning directly, and the comparative learning has research value in industrial control system intrusion detection.
引用
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页数:19
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