@inproceedings{5a8e9e6b1cdc42d3a626ea7acda78491,
title = "Intrusion detection method of industrial control system based on RIPCA-OCSVM",
abstract = "In view of the problem that the intrusion detection method based on One-Class Support Vector Machine (OCSVM) could not detect the outliers within the industrial data, which results in the decision function deviating from the training sample, an anomaly intrusion detection algorithm based on Robust Incremental Principal Component Analysis (RIPCA)-OCSVM is proposed in this paper. The method uses RIPCA algorithm to remove outliers in industrial data sets and realize dimensionality reduction. In combination with the advantages of OCSVM on the single classification problem, an anomaly detection model is established, and the Improved Particle Swarm Optimization (IPSO) is used for model parameter optimization. The simulation results show that the method can efficiently and accurately identify attacks or abnormal behaviors while meeting the real-time requirements of the industrial control system (ICS).",
keywords = "Industrial control system, Intrusion detection, OCSVM, Outlier, RIPCA",
author = "Weiming Tong and Bingbing Liu and Zhongwei Li and Xianji Jin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 3rd IEEE International Conference on Electronic Information Technology and Computer Engineering, EITCE 2019 ; Conference date: 18-10-2019 Through 20-10-2019",
year = "2019",
month = oct,
doi = "10.1109/EITCE47263.2019.9095099",
language = "英语",
series = "2019 IEEE 3rd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1148--1154",
booktitle = "2019 IEEE 3rd International Conference on Electronic Information Technology and Computer Engineering, EITCE 2019",
address = "美国",
}