  Despite the many attempts and approaches for anomaly detection explored over
the years, the automatic detection of rare events in data communication
networks remains a complex problem. In this paper we introduce Net-GAN, a novel
approach to network anomaly detection in time-series, using recurrent neural
networks (RNNs) and generative adversarial networks (GAN). Different from the
state of the art, which traditionally focuses on univariate measurements,
Net-GAN detects anomalies in multivariate time-series, 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. We further exploit the concepts behind generative
models to conceive Net-VAE, a complementary approach to Net-GAN for network
anomaly detection, based on variational auto-encoders (VAE). We evaluate
Net-GAN and Net-VAE in different monitoring scenarios, including anomaly
detection in IoT sensor data, and intrusion detection in network measurements.
Generative models represent a promising approach for network anomaly detection,
especially when considering the complexity and ever-growing number of
time-series to monitor in operational networks.
