Anomaly detection of high-dimensional sparse data based on Ensemble Generative Adversarial Networks

Anomaly detection has drawn public attentions in past decades. However, in a high-dimensional sparse data space, anomaly detection still faces big challenges. In this paper, the Generative Adversarial Network (GAN) combined with Ensemble Learning is introduced to anomaly detection in high-dimensional sparse data. On one hand, the generator of GAN can produce noise data to avoid the data space to be too sparse based on the potential data distribution patterns. On the other hand, the exchanges of the pairing of generators and discriminators can enable the model proposed to learn complex distribution of the data, which may be composed of some various distributions, and to avoid the training process to drop into over-fitting to some extent. Experiments on public datasets show that the proposed approach can improve AUC by 7% compared with traditional GAN based approaches, and by 7.5% to 21.8% compared with other representative anomaly detection approaches.

Automated summary

In this paper, the combination of Generative Adversarial Network (GAN) with Ensemble Learning is introduced for anomaly detection in high-dimensional sparse data. Experimental results show that this approach outperforms traditional GAN-based methods in improving AUC and compared favorably with other representative anomaly detection approaches.