Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

doi:10.1109/TKDE.2021.3102110

	Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals
	Zhang, Yuxin 1,2,3; Chen, Yiqiang 2,3,4; Wang, Jindong 5; Pan, Zhiwen 2,3
	2023-02-01
发表期刊	IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN	1041-4347
卷号	35 期号:2 页码:2118-2132
摘要	Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.
关键词	Anomaly detection Predictive models Data models Autoregressive processes Image reconstruction Forecasting Computational modeling Unsupervised anomaly detection multi-sensor time series convolutional autoencoder attention based BiLSTM
DOI	10.1109/TKDE.2021.3102110
收录类别	SCI
语种	英语
资助项目	Key-Area Research and Development Program of Guangdong Province[2019B010109001] ; Science and Technology Service Network Initiative ; Chinese Academy of Sciences[KFJ-STS-QYZD-2021-11-001] ; Natural Science Foundation of China[61972383] ; Natural Science Foundation of China[61902377] ; Natural Science Foundation of China[61902379]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000914161200075
出版者	IEEE COMPUTER SOC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/19979
专题	中国科学院计算技术研究所期刊论文
通讯作者	Chen, Yiqiang
作者单位	1.Global Energy Interconnect Dev & Cooperat Org, Beijing 100031, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100864, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Peng Cheng Lab PCL, Shenzhen 518066, Peoples R China 5.Microsoft Res Asia, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Yuxin,Chen, Yiqiang,Wang, Jindong,et al. Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(2):2118-2132.
APA	Zhang, Yuxin,Chen, Yiqiang,Wang, Jindong,&Pan, Zhiwen.(2023).Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(2),2118-2132.
MLA	Zhang, Yuxin,et al."Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.2(2023):2118-2132.