Anomaly detection algorithm based on Gaussian mixture variational auto encoder network

Huahua CHEN; Zhe CHEN; Chunsheng GUO; Na YING; Xueyi YE; Jianwu ZHANG

doi:10.11959/j.issn.1000-0801.2021044

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Anomaly detection algorithm based on Gaussian mixture variational auto encoder network

Research and Development | 更新时间：2024-06-05

- Anomaly detection algorithm based on Gaussian mixture variational auto encoder network
- Telecommunications Science Vol. 37, Issue 4, Pages: 54-61(2021)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021044
  CLC： TP393
- Published Online：2021-04，
  
  Published：20 April 2021
- 稿件说明：
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Huahua CHEN, Zhe CHEN, Chunsheng GUO, et al. Anomaly detection algorithm based on Gaussian mixture variational auto encoder network[J]. Telecommunications science, 2021, 37(4): 54-61.
DOI：

Huahua CHEN, Zhe CHEN, Chunsheng GUO, et al. Anomaly detection algorithm based on Gaussian mixture variational auto encoder network[J]. Telecommunications science, 2021, 37(4): 54-61. DOI： 10.11959/j.issn.1000-0801.2021044.

摘要

异常数据是指偏离大量正常数据点的数据，往往会对各类系统产生负面影响，存在较大风险。异常检测作为一种有效的防护手段，能够检测数据中的异常，为各类系统的正常运转提供重要支撑，具有重要的现实意义。提出了一种基于混合高斯变分自编码网络的异常检测算法，该算法首先使用混合高斯先验构建变分自编码器，对输入数据进行特征提取，然后以混合高斯变分自编码器构建深度支持向量网络，压缩特征空间，并寻找最小超球体分离正常数据和异常数据，通过计算数据特征到超球体中心的欧氏距离衡量数据的异常分数，并以此进行异常检测。最后在基准数据集MNIST和Fashion-MNIST上评估了该算法，平均AUC分别达到了0.954和0.937。实验结果表明，所提出的算法取得了较好的异常检测效果。

Abstract

Anomalous data

which deviates from a large number of normal data

has a negative impact and contains a risk on various systems.Anomaly detection can detect anomalies in the data and provide important support for the normal operation of various systems

which has important practical significance.An anomaly detection algorithm based on Gaussian mixture variational auto encoder network was proposed

in which a variational autoencoder was built to extract the features of the input data based on Gaussian mixture distribution

and using this variational autoencoder to construct a deep support vector network to compress the feature space and find the minimum hyper sphere to separate the normal data and the abnormal data.Anomalies can be detected by the score from the Euclidean distance from the feature of data to the center of the hypersphere.The proposed algorithm was evaluated on the benchmark datasets MNIST and Fashion-MNIST

and the corresponding average AUC are 0.954 and 0.937 respectively.The experimental results show that the proposed algorithm achieves preferable effects.

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