One-class classifiers ensemble based anomaly detection scheme for process control systems

被引：24

作者：

Wang, Biao ^{[1
]}

Mao, Zhizhong ^{[1
]}

机构：

[1] Northeastern Univ, Dept Control Theory & Control Engn, Shenyang 110819, Liaoning, Peoples R China

来源：

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL | 2018年 / 40卷 / 12期

关键词：

Anomaly detection; process control system; one-class classification; ensemble learning; INTRUSION DETECTION;

D O I：

10.1177/0142331217724508

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper focuses on the issue of anomaly detection for data in process control systems (PCSs). Considering data features in PCSs, this paper proposes to utilize the notion of one-class classification (OCC). In order to provide a general solution for more types of systems, ensemble learning is combined with OCC models. Two different ensembles of OCC models are proposed based on different scenarios in the process of detection. Performance of the proposed detection scheme is validated via several UCI datasets and two practical PCSs.

引用

页码：3466 / 3476

页数：11

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