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https://hdl.handle.net/20.500.12008/25926
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Title: | On the usage of generative models for network anomaly detection in multivariate time-series. |
Authors: | García González, Gastón Casas, Pedro Fernández, Alicia Gómez, Gabriel |
Type: | Ponencia |
Keywords: | Deep learning, Anomaly detection, Multivariate time-series, Generative models, Generative adversarial networks, Recurrent neural networks, Variational auto-encoders, Artificial intelligence, Machine learning, Networking and internet architecture |
Issue Date: | 2020 |
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. |
Description: | Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica |
Publisher: | ACM |
IN: | WAIN 2020 : Workshop on AI in Networks and Distributed Systems, Milan, Italy, 2-6 nov, page 1-5, 2020 |
Citation: | García González, G., Casas, P., Fernández, A. y otros. On the usage of generative models for network anomaly detection in multivariate time-series [en línea] EN: WAIN 2020 : Workshop on AI in Networks and Distributed Systems, Milan, Italy, 2-6 nov. New York : ACM, 2020. 5 p. |
Appears in Collections: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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