基于VAE-WGAN的多维时间序列异常检测方法

段雪源; 付钰; 王坤

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

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基于VAE-WGAN的多维时间序列异常检测方法

学术论文 | 更新时间：2024-06-05

- 基于VAE-WGAN的多维时间序列异常检测方法
- Multi-dimensional time series anomaly detection method based on VAE-WGAN
- 通信学报 2022年43卷第3期页码：1-13
- 作者机构：
  
  1. 海军工程大学信息安全系，湖北武汉 430033
  2. 信阳师范学院计算机与信息技术学院，河南信阳 464000
  3. 信阳职业技术学院数学与信息工程学院，河南信阳 464000
- 作者简介：
  
  [ "段雪源（1981- ），男，河南开封人，海军工程大学博士生，主要研究方向为人工智能、信息处理、网络安全" ]
  [ "付钰（1982- ），女，湖北武汉人，博士，海军工程大学教授、博士生导师，主要研究方向为信息安全、人工智能" ]
  [ "王坤（1981- ），女，河南信阳人，海军工程大学博士生，主要研究方向为信息安全" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2018YFB0804104)
- DOI：10.11959/j.issn.1000-436x.2022050
  中图分类号： TP391
- 网络首发：2022-03，
  
  纸质出版：2022-03-25
- 稿件说明：
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段雪源, 付钰, 王坤. 基于VAE-WGAN的多维时间序列异常检测方法[J]. 通信学报, 2022,43(3):1-13.

Xueyuan DUAN, Yu FU, Kun WANG. Multi-dimensional time series anomaly detection method based on VAE-WGAN[J]. Journal on Communications, 2022, 43(3): 1-13.
段雪源, 付钰, 王坤. 基于VAE-WGAN的多维时间序列异常检测方法[J]. 通信学报, 2022,43(3):1-13. DOI： 10.11959/j.issn.1000-436x.2022050.

Xueyuan DUAN, Yu FU, Kun WANG. Multi-dimensional time series anomaly detection method based on VAE-WGAN[J]. Journal on Communications, 2022, 43(3): 1-13. DOI： 10.11959/j.issn.1000-436x.2022050.

摘要

针对传统半监督深度异常检测模型对非平衡多维数据分布学习能力不足及模型训练困难等问题，提出一种基于VAE-WGAN架构的多维时间序列异常检测方法，利用VAE作为WGAN的生成器，使用Wasserstein距离作为模型拟合分布与待测数据真实分布之间的度量，学习复杂的高维数据分布。利用滑动窗口划分时间序列，使用正常序列数据训练模型；根据待测序列在训练好的模型中的异常得分，结合自适应阈值技术进行异常判定。实验表明，该方法具有模型容易训练且稳定性强的特点，并且在精确率、召回率､F1值等异常检测性能指标上，比现有的生成式异常检测模型有明显提升。

Abstract

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.

关键词

Keywords

references

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