Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series.
Resumen:
We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial learning (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate timeseries, 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
| 2020 | |
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Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/25478 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522896530112512 |
|---|---|
| author | García González, Gastón |
| author2 | Casas, Pedro Fernández, Alicia Gómez, Gabriel |
| author2_role | author author author |
| author_facet | García González, Gastón Casas, Pedro Fernández, Alicia Gómez, Gabriel |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | García González Gastón, Universidad de la República (Uruguay). Facultad de Ingeniería. Casas Pedro, AIT Austrian Institute of Technology Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería. Gómez Gabriel, Universidad de la República (Uruguay). Facultad de Ingeniería. |
| dc.creator.none.fl_str_mv | García González, Gastón Casas, Pedro Fernández, Alicia Gómez, Gabriel |
| dc.date.accessioned.none.fl_str_mv | 2020-10-07T15:42:25Z |
| dc.date.available.none.fl_str_mv | 2020-10-07T15:42:25Z |
| dc.date.issued.none.fl_str_mv | 2020 |
| dc.description.abstract.none.fl_txt_mv | We introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial learning (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate timeseries, 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 |
| dc.description.es.fl_txt_mv | Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica |
| dc.format.extent.es.fl_str_mv | 1 p. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | García González, G., Casas, P., Fernández, A. y otros. Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series [en línea] EN: TMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun. [S.l.] : IEEE/IFIP, 2020. 1 p. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/25478 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | IEEE/IFIP |
| dc.relation.ispartof.es.fl_str_mv | TMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun. |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| dc.title.none.fl_str_mv | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| dc.type.es.fl_str_mv | Ponencia |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/conferenceObject |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica |
| eu_rights_str_mv | openAccess |
| format | conferenceObject |
| id | COLIBRI_3f6b226a2d81031803db85e3edc30f1e |
| identifier_str_mv | García González, G., Casas, P., Fernández, A. y otros. Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series [en línea] EN: TMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun. [S.l.] : IEEE/IFIP, 2020. 1 p. |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/25478 |
| publishDate | 2020 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | García González Gastón, Universidad de la República (Uruguay). Facultad de Ingeniería.Casas Pedro, AIT Austrian Institute of TechnologyFernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería.Gómez Gabriel, Universidad de la República (Uruguay). Facultad de Ingeniería.2020-10-07T15:42:25Z2020-10-07T15:42:25Z2020García González, G., Casas, P., Fernández, A. y otros. Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series [en línea] EN: TMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun. [S.l.] : IEEE/IFIP, 2020. 1 p.https://hdl.handle.net/20.500.12008/25478Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería EléctricaWe introduce Net-GAN, a novel approach to network anomaly detection in time-series, using recurrent neural networks (RNNs) and generative adversarial learning (GAN). Different from the state of the art, which traditionally focuses on univariate measurements, Net-GAN detects anomalies in multivariate timeseries, 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 dataSubmitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2020-10-05T18:48:20Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20a.pdf: 172163 bytes, checksum: e2ffd8c7e39f4e4a9d25bd0b49485d05 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2020-10-07T14:50:41Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20a.pdf: 172163 bytes, checksum: e2ffd8c7e39f4e4a9d25bd0b49485d05 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2020-10-07T15:42:25Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20a.pdf: 172163 bytes, checksum: e2ffd8c7e39f4e4a9d25bd0b49485d05 (MD5) Previous issue date: 20201 p.application/pdfenengIEEE/IFIPTMA Conference 2020, Network Traffic Measurement and Analysis Conference, Berlin, Germany, 8-12 jun.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)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Computing methodologiesAnomaly detectionMachine learning algorithmsMultivariate time-seriesGenerative modelsGANLSTMNet-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series.Ponenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGarcía González, GastónCasas, PedroFernández, AliciaGómez, GabrielORIGINALGCFG20a.pdfGCFG20a.pdfapplication/pdf163461http://localhost:8080/xmlui/bitstream/20.500.12008/25478/6/GCFG20a.pdf651562b42409b42c9f4ec034422ff4a6MD56LICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/25478/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. García González, Gastón Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| status_str | publishedVersion |
| title | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| title_full | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| title_fullStr | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| title_full_unstemmed | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| title_short | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| title_sort | Net-GAN : Recurrent generative adversarial networks for network anomaly detection in multivariate time-series. |
| topic | Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| url | https://hdl.handle.net/20.500.12008/25478 |
