RVAE-ABFA : Robust Anomaly Detection for HighDimensional Data Using Variational Autoencoder

Yuda Gao, Bin Shi, Bo Dong, Yan Chen, Lingyun Mi, Zhiping Huang, Yuanyuan Shi

Abstract: The curse of dimensionality is a fundamental difficulty in anomaly detection for high dimensional data. To deal with this problem, the autoencoder based approach is an elegant solution. However, existing works require a clean training dataset that is not always guaranteed in real scenarios. In this paper, we propose a novel anomaly detection method named RVAE-ABFA (robust variational autoencoder with attention based feature adaptation for high dimensional data anomaly detection), which significantly improves the anomaly detection performance when training data is contaminated. Rather than only utilize reconstruction error, we take the learned low dimensional embeddings generated by variational autoencoder into consideration. In RVAE-ABFA, the learned low dimensional embeddings are helpful to detect anomalies in contaminated data because of the ability of variational inference. We also propose an ABFA (attention based feature adaptation) mechanism to adjust the weights of low dimensional embeddings and reconstruction error. Furthermore, we adopt the adversarial training criterion to perform variational inference by the adversarial network named RAAE-ABFA (robust adversarial autoencoder with attention based feature adaptation for high dimensional data anomaly detection) in which we can generate extra samples when training data is not enough. Experimental results on several benchmark datasets show that the proposed method significantly outperforms state-of-the-art unsupervised anomaly detection methods and is more robust when training data is contaminated.