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

共 50 条

[21] One-class classifiers
Brereton, Richard G.
JOURNAL OF CHEMOMETRICS, 2011, 25 (05) : 225 - 246
[22] Clustering-Based Ensemble of One-Class Classifiers for Hyperspectral Image Segmentation
Krawczyk, Bartosz
Wozniak, Michal
Cyganek, Boguslaw
HYBRID ARTIFICIAL INTELLIGENCE SYSTEMS, HAIS 2014, 2014, 8480 : 678 - 688
[23] Double Behavior Characteristics for One-Class Classification Anomaly Detection in Networked Control Systems
Wan, Ming
Shang, Wenli
Zeng, Peng
IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2017, 12 (12) : 3011 - 3023
[24] Acoustic Sensor Based Activity Recognition Using Ensemble of One-Class Classifiers
Tripathi, Achyut Mani
Baruah, Diganta
Baruah, Rashmi Dutta
2015 IEEE INTERNATIONAL CONFERENCE ON EVOLVING AND ADAPTIVE INTELLIGENT SYSTEMS (EAIS), 2015,
[25] Ensemble of One-Class Classifiers for Detecting Faults in Induction Motors
Zare, Shokoofeh
Razavi-Far, Roozbeh
Saif, Mehrdad
Zarei, Jafar
2018 IEEE CANADIAN CONFERENCE ON ELECTRICAL & COMPUTER ENGINEERING (CCECE), 2018,
[26] Anomaly detection for medical images based on a one-class classification
Wei, Qi
Ren, Yinhao
Hou, Rui
Shi, Bibo
Lo, Joseph Y.
Carin, Lawrence
MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS, 2018, 10575
[27] Active anomaly detection based on deep one-class classification
Kim, Minkyung
Kim, Junsik
Yu, Jongmin
Choi, Jun Kyun
PATTERN RECOGNITION LETTERS, 2023, 167 : 18 - 24
[28] Anomaly detection based on one-class intelligent techniques over a control level plant
Jove, Esteban
Casteleiro-Roca, Jose-Luis
Quintian, Hector
Simic, Dragan
Mendez-Perez, Juan-Albino
Calvo-Rolle, Jose Luis
LOGIC JOURNAL OF THE IGPL, 2020, 28 (04) : 502 - 518
[29] Multi-class classification via heterogeneous ensemble of one-class classifiers
Kang, Seokho
Cho, Sungzoon
Rang, Pilsung
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2015, 43 : 35 - 43
[30] Improving one-class SVM for anomaly detection
Li, KL
Huang, HK
Tian, SF
Xu, W
2003 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-5, PROCEEDINGS, 2003, : 3077 - 3081

← 1 2 3 4 5 →