On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series

• URL: http://arxiv.org/abs/2010.08286v1
• Date: Fri, 16 Oct 2020 10:22:25 GMT
• Title: On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series
• Authors: Gast\'on Garc\'ia Gonz\'alez, Pedro Casas, Alicia Fern\'andez, and Gabriel G\'omez
• Abstract summary: We introduce Net-GAN, a novel approach to network anomaly detection in time-series. We exploit the concepts behind generative models to conceive Net-VAE, a complementary approach to Net-GAN. We evaluate Net-GAN and Net-VAE in different monitoring scenarios, including anomaly detection in IoT sensor data, and intrusion detection in network measurements.
• Score: 3.1790432590377242
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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