DataVXApplied SciencesPaper
journal article·2023-08-14Open access

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

Chengcheng Li, Fei Li, Liyan Zhang, Aimin Yang, Zhibin Hu, Ming He

Applied Sciences· Volume 13· Issue 16
Publisher pageOpen full text
Source abstract

Abstract

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.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Anomaly Detection Techniques and Applications · Machine Learning and ELM · Network Security and Intrusion Detection

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Hebei Key Laboratory of Data Science and Application, North China University of Science and Technology, Tangshan 063210, China

The Key Laboratory of Engineering Computing in Tangshan City, Tangshan 063210, China

College of Science, North China University of Science and Technology, Tangshan 063210, China

Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes, North China University of Science and Technology, Tangshan 063210, China

Shanxi Jianlong Industrial Co., Ltd., Yuncheng 044000, China

College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Tangshan Intelligent Industry and Image Processing Technology Innovation Center, North China University of Science and Technology, Tangshan 063210, China

Funding

National Natural Science Foundation of China · Hebei Provincial Natural Science Foundation of China · Scientific Basic Research Projects (Natural Sciences) · Hebei Natural Science Foundation Project

References (22)

Show source-provided references
1. Hybrid ontology for safety, security, and dependability risk assessments and Security Threat Analysis (STA) method for industrial control systems10.1016/j.ress.2021.108270 ↗
2. Adversarial attacks on machine learning cybersecurity defences in industrial control systems
3. A survey on attack detection, estimation and control of industrial cyber–physical systems10.1016/j.isatra.2021.01.036 ↗
4. Cybersecurity awareness in the context of the Industrial Internet of Things: A systematic literature review10.1016/j.compind.2022.103614 ↗
5. DeepFed: Federated deep learning for intrusion detection in industrial cyber–physical systems10.1109/TII.2020.3023430 ↗
6. A lightweight supervised intrusion detection mechanism for IoT networks10.1016/j.future.2021.09.027 ↗
7. Deep learning in IoT intrusion detection. Journal of network and systems management10.1007/s10922-021-09621-9 ↗
8. Capturing-the-invisible (CTI): Behavior-based attacks recognition in IoT-oriented industrial control systems10.1109/ACCESS.2020.2998983 ↗
9. Cross-Domain Industrial Intrusion Detection Deep Model Trained With Imbalanced Data10.1109/JIOT.2022.3201888 ↗
10. Khan, M.A. (2021). HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System. Processes, 9.10.3390/pr9050834 ↗
11. Detecting stealthy attacks on industrial control systems using a permutation entropy-based method10.1016/j.future.2018.07.027 ↗
12. An intrusion detection method for industrial control systems based on bidirectional simple recurrent unit10.1016/j.compeleceng.2021.107049 ↗
13. Anomaly detection for industrial control system based on autoencoder neural network10.1155/2020/8897926 ↗
14. Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations10.1016/j.ijcip.2022.100516 ↗
15. Self-supervised learning: Generative or contrastive
16. A simple framework for contrastive learning of visual representations
17. He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R. (2020, January 13–19). Momentum contrast for unsupervised visual representation learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR42600.2020.00975 ↗
18. Bootstrap your own latent-a new approach to self-supervised learning
19. Chen, X., and He, K. (2020, January 13–19). Exploring simple siamese representation learning. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.10.1109/CVPR46437.2021.01549 ↗
20. Unsupervised learning of visual features by contrasting cluster assignments
21. Research on expansion and classification of imbalanced data based on SMOTE algorithm10.1038/s41598-021-03430-5 ↗
22. Shafiq, M., and Gu, Z. (2022). Deep Residual Learning for Image Recognition: A Survey. Appl. Sci., 12.10.3390/app12188972 ↗