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
收藏
页码：498 / 507
页数：10
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