Shuffle based Anomaly Detection in Multi-state System

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	Shuffle based Anomaly Detection in Multi-state System
	Cong Y(丛杨); Hou DD(侯冬冬); Xu XW(徐晓伟); Sun G(孙干)
	2017
会议名称	7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017)
会议日期	July 31 - August 4, 2017
会议地点	Hawaii, USA
关键词	Anomaly detection Multi-state system Step changes Shuffle
页码	874-879
通讯作者	Hou DD(侯冬冬)
中文摘要	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.
产权排序	1
会议录	7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017)
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-5386-0489-2
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/21352]
专题	沈阳自动化研究所_机器人学研究室
作者单位	1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Cong Y,Hou DD,Xu XW,et al. Shuffle based Anomaly Detection in Multi-state System[C]. 见:7th Annual IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER 2017). Hawaii, USA. July 31 - August 4, 2017.