Título:
On the usage of generative models for network anomaly detection in multivariate time-series.
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Autor:
García González, Gastón
Casas, Pedro
Fernández, Alicia
Gómez, Gabriel
Casas, Pedro
Fernández, Alicia
Gómez, Gabriel
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Tipo de documento:
Ponencia
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Palabras clave:
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
Anomaly detection
Multivariate time-series
Generative models
Generative adversarial networks
Recurrent neural networks
Variational auto-encoders
Artificial intelligence
Machine learning
Networking and internet architecture
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Año de publicación:
2020
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Resumen:
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.
Descripción:
Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica
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Editorial:
ACM
EN:
WAIN 2020 : Workshop on AI in Networks and Distributed Systems, Milan, Italy, 2-6 nov, page 1-5, 2020
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Citación:
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.
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| GCFG20b.pdf | Versión aceptada | 728.25 KB | Adobe PDF |