基于混合高斯变分自编码网络的异常检测算法

doi:10.11959/j.issn.1000-0801.2021044

Abstract

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.

Key words: anomaly detection, variational autoencoder, Gaussian mixture distribution, hypersphere

CLC Number:

TP393

Huahua CHEN, Zhe CHEN, Chunsheng GUO, Na YING, Xueyi YE, Jianwu ZHANG. Anomaly detection algorithm based on Gaussian mixture variational auto encoder network[J]. Telecommunications Science, 2021, 37(4): 54-61.

Figures/Tables 6

一般类别	OC-SVM^[21]	IF^[8]	DCAE^[22]	ANOGAN^[23]	本文算法
0	98.6%±0.0%	98.0%±0.0%	97.6%±0.7%	96.6%±1.3%	$99 . 0 %$ ±0.4%
1	99.5%±0.0%	97.3%±0.0%	98.3%±0.6%	99.2%±0.6%	$99 . 7 %$ ±0.1%
2	82.5%±0.1%	88.6%±0.0%	85.4%±2.4%	85.0%±2.9%	$94 . 4 %$ ±0.4%
3	88.1%±0.0%	89.9%±0.0%	86.7%±0.9%	88.7%±2.1%	$94 . 3 %$ ±0.3%
4	$94 . 9 %$ ±0.0%	92.7%±0.0%	86.5%±2.0%	89.4%±1.3%	93.1%±0.5%
5	77.1%±0.0%	85.5%±0.0%	78.2%±2.7%	88.3%±2.9%	$91 . 2 %$ ±0.4%
6	96.5%±0.0%	95.6%±0.0%	94.6%±0.5%	94.7%±2.7%	$98 . 3 %$ ±0.2%
7	93.7%±0.0%	92.0%±0.0%	92.3%±1.0%	$94 . 5 %$ ±1.8%	94.1%±0.3%
8	88.9%±0.0%	89.9%±0.0%	86.5%±1.6%	84.9%±2.1%	$93 . 9 %$ ±0.8%
9	94.1%±0.0%	94.5%±0.0%	90.4%±1.8%	92.4%±1.1%	$95 . 7 %$ ±0.6%
平均值	91.3%±0.0%	92.3%±0.0%	89.7%±1.4%	91.3%±1.9%	$95 . 4 %$ ±0.4%

一般类别	OC-SVM^[21]	IF^[8]	DCAE^[22]	ANOGAN^[23]	本文算法
0（T恤）	91.4%±0.0%	91.3%±0.0%	95.2%±0.4%	95.8%±0.5%	93.9%±0.3%
1（裤子）	98.2%±0.0%	97.5%±0.0%	98.3%±0.3%	98.5%±0.6%	$98 . 6 %$ ±0.2%
2（套衫）	88.2%±0.1%	87.4%±0.0%	84.3%±0.7%	88.8%±0.9%	$91 . 5 %$ ±0.3%
3（裙子）	91.6%±0.0%	92.2%±0.0%	89.7%±0.5%	88.7%±2.1%	$92 . 4 %$ ±0.7%
4（外套）	89.3%±0.0%	90.8%±0.0%	86.5%±1.3%	90.4%±0.8%	$93 . 5 %$ ±0.3v
5（凉鞋）	89.1%±0.0%	92.6%±0.0%	82.6%±0.7%	90.7%±0.6%	$92 . 5 %$ ±0.5%
6（衬衫）	84.5%±0.1%	80.0%±0.0%	79.3%±0.4%	84.3%±1.4%	$84 . 6 %$ ±1.2%
7（球鞋）	98.5%±0.0%	98.3%±0.0%	92.3%±0.6%	94.5%±0.8%	$98 . 6 %$ ±0.2%
8（皮包）	89.5%±0.0%	88.4%±0.0%	91.7%±0.9%	$94 . 1 %$ ±1.2%	92.1%±1.5%
9（靴子）	97.7%±0.0%	97.6%±0.0%	91.2%±0.5%	92.6%±0.7%	$98 . 8 %$ ±0.1%
平均值	91.8%±0.0%	91.6%±0.0%	89.1%±0.6%	91.6%±1.1%	$93 . 7 %$ ±0.5%

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真值	预测值
真值	正常	异常
正常	TP（真正例）	FN（假反例）
异常	FP（假正例）	TN（真反例）

Anomaly detection algorithm based on Gaussian mixture variational auto encoder network

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