An unsupervised anomaly detection approach DGANs is proposed based on ensemble GANs with Dropout. The comparisons with representative approaches on 10 public datasets show it has advantages in accuracy, recall and F1 scores. DGANs can address the overfitting problem in ensemble GANs training on high-dimensional datasets.

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.