A hybrid intelligent classifier for anomaly detection

被引：4

作者：

Jove, Esteban ^{[1
]}

Casado-Vara, Roberto ^{[2
]}

Casteleiro-Roca, Jose-Luis ^{[1
]}

Mendez Perez, Juan Albino ^{[3
]}

Vale, Zita ^{[4
]}

Luis Calvo-Rolle, Jose ^{[1
]}

机构：

[1] Univ A Coruna, Dept Ind Engn, Avda 19 Febrero S-N, Ferrol 15405, A Coruna, Spain

[2] IoT Digital Innovat Hub, BISITE Res Grp, Edificio Multiusos I D I, Salamanca 37007, Spain

[3] Univ La Laguna, Dept Comp Sci & Syst Engn, Avda Astrofis Francisco Sanchez S-N, San Cristobal la Laguna 38200, Spain

[4] Polytech Porto ISEP IPP, Inst Engn, Res Grp Intelligent Engn & Comp Adv Innovat & Dev, Rua Dr Antonio Bernardino de Almeida 431, P-4200072 Porto, Portugal

来源：

NEUROCOMPUTING | 2021年 / 452卷

关键词：

One-class; Outlier detection; SVDD; Autoencoder; PCA; APE; SYSTEM; CURVE;

D O I：

10.1016/j.neucom.2019.12.138

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The present research is focused on the use of intelligent techniques to perform anomaly detection. This task represents a special concern in complex systems that operate in different regimes. Then, this work proposes a hybrid intelligent classifier based on one-class techniques, capable of detecting anomalies of the different operating ranges. The proposal is implemented over an industrial plant designed to control the water level in a tank, taking into consideration three different operating points. The hybrid classifier is validated by using real anomalies, obtaining successful results. (c) 2020 Elsevier B.V. All rights reserved.

引用

页码：498 / 507

页数：10

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