Multi-dimensional time series anomaly detection method based on VAE-WGAN

被引：0

作者：

Duan X. ^{[1
,2
]}

Fu Y. ^{[1
]}

Wang K. ^{[1
,3
]}

机构：

[1] Department of Information Security, Naval University of Engineering, Wuhan

[2] College of Computer and Information Technology, Xinyang Normal University, Xinyang

[3] School of Mathematics and Information Engineering, Xinyang Vocational and Technical College, Xinyang

来源：

Tongxin Xuebao/Journal on Communications | 2022年 / 43卷 / 03期

基金：

国家重点研发计划;

关键词：

Anomaly detection; Time series data; Variational auto-encoder; Wasserstein generative adversarial network;

D O I：

10.11959/j.issn.1000-436x.2022050

中图分类号：

学科分类号：

摘要：

As the deficiency of learning ability of traditional semi-supervised depth anomaly detection model to unbalanced multidimensional data distribution and the difficulty of model training, a multi-dimensional time series anomaly detection method based on VAE-WGAN architecture was proposed. VAE was used as a generator of WGAN. The Wasserstein distance was used as a measure between the model fitting distribution and the real distribution of the data to be measured, complex and high-dimensional data distributions could be learned. A sliding window was applied to divide the time series, the normal sequence data were used to train the model. According to the abnormal score of the waiting test sequence in the trained model, the anomaly was judged with adaptive threshold technology. The experimental results show that the model is easy to train and stable, and has obvious improvement over the existing generative anomaly detection model in accuracy, recall rate, F1 score and other anomaly detection performance indicators. © 2022, Editorial Board of Journal on Communications. All right reserved.

引用

页码：1 / 13

页数：12

共 28 条

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