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Shuffle based Anomaly Detection in Multi-state System
Hou DD(侯冬冬); Cong Y(丛杨); Sun G(孙干); Xu XW(徐晓伟)
Department机器人学研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages874-879
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873454
WOS IDWOS:000447628700159
Contribution Rank1
ISBN978-1-5386-0489-2
KeywordAnomaly Detection Multi-state System Step Changes Shuffle
Abstract

The anomaly events are defined as the points that rare and diverse from the other points in feature space. Conventional anomaly detection methods usually find low-probability events with a learned a probability distribution model, or evaluate the testing samples with the local density of the testing samples. Multi-state system usually has multiple normal states, and state changes at unpredictable points caused by the daily operation such as feed, outlet, flow control, etc. For the multistate system, collecting enough data that contain all possible states are challenging or impossible to users. Furthermore, conventional anomaly detection methods are sensitive to the context of training datasets or the unpredicted phased changes of the testing datasets, or just consider the local density of the testing samples. Motivated by this problem, we transform the model learning problem to a distinction learning problem that learns the familiarity of each testing samples. In order to reduce the effects of the phased changes, we randomly shuffle the testing dataset and use a sliding window to evaluate the familiarity of the testing samples with one-class Support Vector Machine (SVM) method. Our contributions include: (1) reducing the requirement of the prior knowledge; (2) handling the phased changes of the testing datasets, (3) considering the global familiarity of the testing samples. Our proposed method is evaluated on the synthetic datasets, and the real datasets, and experiments results show that our proposed method is superior than the state-of-theart methods.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/21352
Collection机器人学研究室
Corresponding AuthorHou DD(侯冬冬)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Hou DD,Cong Y,Sun G,et al. Shuffle based Anomaly Detection in Multi-state System[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:874-879.
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