Unsupervised Log Anomaly Detection Method Based on VAE-GAN

WANG Bo-chao; WANG Ya-hui; ZENG Zhao-hu; ZHAO Jian-hui

Computer & Telecommunication ›› 2025, Vol. 1 ›› Issue (4) : 23-29.

WANG Bo-chao, WANG Ya-hui, ZENG Zhao-hu, ZHAO Jian-hui

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Abstract

This paper proposes an unsupervised log anomaly detection method based on an improved variational autoencoder generative adversarial network (VAE-GAN) to address the issues of instability and interdependence in log sequence data. The proposed model combines the advantages of GAN and VAE by embedding the temporal convolutional network module into the encoder, decoder, and discriminator, effectively capturing the distribution of log sequence data and optimizing the sequence mapping in the latent space, thereby achieving high-precision reconstruction of normal log sequences. The model continuously improves the reconstruction ability of the variational autoencoder through adversarial training mechanism, enabling it to identify abnormal patterns in the log more accurately. The experimental results show that compared with other unsupervised methods, this method has better performance on public log datasets.

Key words

log anomaly detection / generate adversarial networks / variational autoencoder / time convolutional network

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WANG Bo-chao, WANG Ya-hui, ZENG Zhao-hu, ZHAO Jian-hui. Unsupervised Log Anomaly Detection Method Based on VAE-GAN[J]. Computer & Telecommunication. 2025, 1(4): 23-29

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