An Industrial Control Intrusion Detection Method Combining Semi-supervised LDA and PSO-SVM
- 10.2991/cnci-19.2019.52How to use a DOI?
- PCA, LDA, industrial control system, intrusion detection, SVM
In order to improve the accuracy of industrial control intrusion detection, this paper proposes a fusion semi-supervised LDA and PSO-SVM method, using the cumulative contribution rate ω to determine the principal component analysis (PCA) accounted for the proportion of semi-supervised LDA algorithm. The experimental results show that compared with a single PCA or LDA and PSO-SVM combination, this method of combining semi-supervised LDA and PSO-SVM has advantages, high accuracy of anomaly detection, and low false positive rate.
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Liu Zhan AU - Jie Ling AU - Peng Lin PY - 2019/05 DA - 2019/05 TI - An Industrial Control Intrusion Detection Method Combining Semi-supervised LDA and PSO-SVM BT - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019) PB - Atlantis Press SP - 363 EP - 370 SN - 2352-538X UR - https://doi.org/10.2991/cnci-19.2019.52 DO - 10.2991/cnci-19.2019.52 ID - Zhan2019/05 ER -