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Campo DC | Valor | Lengua/Idioma |
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dc.contributor.author | García González, Gastón | - |
dc.contributor.author | Casas, Pedro | - |
dc.contributor.author | Fernández, Alicia | - |
dc.contributor.author | Gómez, Gabriel | - |
dc.date.accessioned | 2020-11-20T16:11:33Z | - |
dc.date.available | 2020-11-20T16:11:33Z | - |
dc.date.issued | 2020 | - |
dc.identifier.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. | es |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/25926 | - |
dc.description | Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica | es |
dc.description.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. | en |
dc.format.extent | 5 p. | es |
dc.format.mimetype | application/pdf | en |
dc.language.iso | en | es |
dc.publisher | ACM | es |
dc.relation.ispartof | WAIN 2020 : Workshop on AI in Networks and Distributed Systems, Milan, Italy, 2-6 nov, page 1-5, 2020 | en |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.subject | Deep learning | en |
dc.subject | Anomaly detection | en |
dc.subject | Multivariate time-series | en |
dc.subject | Generative models | en |
dc.subject | Generative adversarial networks | en |
dc.subject | Recurrent neural networks | en |
dc.subject | Variational auto-encoders | en |
dc.subject | Artificial intelligence | en |
dc.subject | Machine learning | en |
dc.subject | Networking and internet architecture | en |
dc.title | On the usage of generative models for network anomaly detection in multivariate time-series. | en |
dc.type | Ponencia | es |
dc.contributor.filiacion | García González Gastón, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.contributor.filiacion | Casas Pedro, AIT Austrian Institute of Technology | - |
dc.contributor.filiacion | Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.contributor.filiacion | Gómez Gabriel, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
Aparece en las colecciones: | Publicaciones académicas y científicas - Instituto de Ingeniería Eléctrica |
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