Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series.

Abstract

We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial learning (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate timeseries, exploiting temporal dependencies through RNNs. Net- GAN discovers the underlying distribution of the baseline, multivariate data, without making any assumptions on its nature, offering a powerful approach to detect anomalies in complex, difficult to model network monitoring data