Network anomaly detection with Net-GAN, a generative adversarial network for analysis of 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 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.
| 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/20.500.12008/25470 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522896517529600 |
|---|---|
| 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 | 2020-10-06T18:28:03Z |
| dc.date.available.none.fl_str_mv | 2020-10-06T18:28:03Z |
| 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 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. |
| 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 | 3 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. 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 |
| dc.identifier.doi.none.fl_str_mv | 10.1145/3405837.3411393 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/25470 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | ACM |
| dc.relation.ispartof.es.fl_str_mv | ACM Special Interest Group on Data Communication (SIGCOMM ’20 Demos and Posters), Nueva York, NY, USA, 10-14 aug, page 1-3 |
| 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.en.fl_str_mv | Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| dc.title.none.fl_str_mv | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of 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 |
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| identifier_str_mv | 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 10.1145/3405837.3411393 |
| 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:20.500.12008/25470 |
| 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.2020-10-06T18:28:03Z2020-10-06T18:28:03Z2020García 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/10.1145/3405837.3411393https://hdl.handle.net/20.500.12008/2547010.1145/3405837.3411393Transferencia 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 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.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2020-10-05T18:27:34Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20.pdf: 249675 bytes, checksum: e0ad3c1ebf20cf7661885efed1807ea3 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2020-10-06T18:26:28Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20.pdf: 249675 bytes, checksum: e0ad3c1ebf20cf7661885efed1807ea3 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@fic.edu.uy) on 2020-10-06T18:28:03Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG20.pdf: 249675 bytes, checksum: e0ad3c1ebf20cf7661885efed1807ea3 (MD5) Previous issue date: 20203 p.application/pdfenengACMACM Special Interest Group on Data Communication (SIGCOMM ’20 Demos and Posters), Nueva York, NY, USA, 10-14 aug, page 1-3Las 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 modelsGANLSTMNetwork anomaly detection with Net-GAN, a generative adversarial network for analysis of 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, GabrielLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/25470/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/25470/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; 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- Universidad de la Repúblicafalse |
| spellingShingle | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. García González, Gastón Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| status_str | publishedVersion |
| title | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| title_full | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| title_fullStr | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| title_full_unstemmed | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| title_short | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| title_sort | Network anomaly detection with Net-GAN, a generative adversarial network for analysis of multivariate time-series. |
| topic | Computing methodologies Anomaly detection Machine learning algorithms Multivariate time-series Generative models GAN LSTM |
| url | https://hdl.handle.net/20.500.12008/25470 |
