Título:
Network anomaly detection with Net-GAN, a generative adversarial network for analysis of 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:
Computing methodologies
Anomaly detection
Machine learning algorithms
Multivariate time-series
Generative models
GAN
LSTM
Anomaly detection
Machine learning algorithms
Multivariate time-series
Generative models
GAN
LSTM
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Año de publicación:
2020
Contenido:
Resumen:
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 present preliminary detection results in different monitoring scenarios, including anomaly detection in sensor data, and intrusion detection in network measurements.
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:
ACM Special Interest Group on Data Communication (SIGCOMM ’20 Demos and Posters), Nueva York, NY, USA, 10-14 aug, page 1-3
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Citación:
García González, G., Casas, P., Fernández, A. y otros. Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. [en línea] EN : ACM Special Interest Group on Data Communication (SIGCOMM ’20 Demos and Posters), Nueva York, USA, 10-14 aug. Nueva York : ACM, 2020. 3 p. DOI : https://doi.org/10.1145/3405837.3411393
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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 | ||
|---|---|---|---|---|---|
| GCFG20.pdf | Versión aceptada | 243.82 KB | Adobe PDF |