Real time anomaly detection in network traffic time series
Resumen:
Anomaly detection is a relevant field of study for many applications and contexts. In this paper we focus in on-line anomaly detection on unidimensional time series provided by different network operator equipments. We have implemented two detection methods, we have optimized them for on-line processing and we have adapted them for integration into a testbed of a well known Hadoop big data platform. We have analyzed the behavior of both methods for the particular datasets available but we also have applied the methods to a publicly available labeled datasets obtaining good results.
| 2018 | |
|
Anomaly detection Kalman filter Hadoop |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/43953 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución (CC - By 4.0) |
| _version_ | 1807523062588899328 |
|---|---|
| author | Martínez Tagliafico, Sergio |
| author2 | García González, Gastón Fernández, Alicia Gómez, Gabriel Acuña, José |
| author2_role | author author author author |
| author_facet | Martínez Tagliafico, Sergio García González, Gastón Fernández, Alicia Gómez, Gabriel Acuña, José |
| author_role | author |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Martínez Tagliafico Sergio, Universidad de la República (Uruguay). Facultad de Ingeniería García González Gastón, Universidad de la República (Uruguay). Facultad de Ingeniería Fernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería Gómez Gabriel, Universidad de la República (Uruguay). Facultad de Ingeniería Acuña José, Universidad de la República (Uruguay). Facultad de Ingeniería |
| dc.creator.none.fl_str_mv | Martínez Tagliafico, Sergio García González, Gastón Fernández, Alicia Gómez, Gabriel Acuña, José |
| dc.date.accessioned.none.fl_str_mv | 2024-05-30T19:55:03Z |
| dc.date.available.none.fl_str_mv | 2024-05-30T19:55:03Z |
| dc.date.issued.none.fl_str_mv | 2018 |
| dc.description.abstract.none.fl_txt_mv | Anomaly detection is a relevant field of study for many applications and contexts. In this paper we focus in on-line anomaly detection on unidimensional time series provided by different network operator equipments. We have implemented two detection methods, we have optimized them for on-line processing and we have adapted them for integration into a testbed of a well known Hadoop big data platform. We have analyzed the behavior of both methods for the particular datasets available but we also have applied the methods to a publicly available labeled datasets obtaining good results. |
| 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.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Martínez Tagliafico, S, García González, G, Fernández, A, Gómez, G y Acuña, J. "Real time anomaly detection in network traffic time series" [en línea] ITISE 2018. International conference on Time Series and Forecasting, Granada, Spain, 19-21 set. 2018. |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/43953 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.relation.ispartof.es.fl_str_mv | ITISE 2018 : International conference on Time Series and Forecasting, Granada, Spain, 19-21 set. 2018 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución (CC - By 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 | Anomaly detection Kalman filter Hadoop |
| dc.title.none.fl_str_mv | Real time anomaly detection in network traffic 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_1cf2fb6720c15e00d6fcea6d94136e48 |
| identifier_str_mv | Martínez Tagliafico, S, García González, G, Fernández, A, Gómez, G y Acuña, J. "Real time anomaly detection in network traffic time series" [en línea] ITISE 2018. International conference on Time Series and Forecasting, Granada, Spain, 19-21 set. 2018. |
| 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:20.500.12008/43953 |
| publishDate | 2018 |
| 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 (CC - By 4.0) |
| spelling | Martínez Tagliafico Sergio, Universidad de la República (Uruguay). Facultad de IngenieríaGarcía González Gastón, Universidad de la República (Uruguay). Facultad de IngenieríaFernández Alicia, Universidad de la República (Uruguay). Facultad de IngenieríaGómez Gabriel, Universidad de la República (Uruguay). Facultad de IngenieríaAcuña José, Universidad de la República (Uruguay). Facultad de Ingeniería2024-05-30T19:55:03Z2024-05-30T19:55:03Z2018Martínez Tagliafico, S, García González, G, Fernández, A, Gómez, G y Acuña, J. "Real time anomaly detection in network traffic time series" [en línea] ITISE 2018. International conference on Time Series and Forecasting, Granada, Spain, 19-21 set. 2018.https://hdl.handle.net/20.500.12008/43953Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería EléctricaAnomaly detection is a relevant field of study for many applications and contexts. In this paper we focus in on-line anomaly detection on unidimensional time series provided by different network operator equipments. We have implemented two detection methods, we have optimized them for on-line processing and we have adapted them for integration into a testbed of a well known Hadoop big data platform. We have analyzed the behavior of both methods for the particular datasets available but we also have applied the methods to a publicly available labeled datasets obtaining good results.Submitted by Seroubian Mabel (mabel.seroubian@seciu.edu.uy) on 2024-05-30T19:54:15Z No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MGFGA18.pdf: 2944072 bytes, checksum: d647e370a40a5a28360e7d377a1ab678 (MD5)Approved for entry into archive by Seroubian Mabel (mabel.seroubian@seciu.edu.uy) on 2024-05-30T19:54:36Z (GMT) No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MGFGA18.pdf: 2944072 bytes, checksum: d647e370a40a5a28360e7d377a1ab678 (MD5)Made available in DSpace by Seroubian Mabel (mabel.seroubian@seciu.edu.uy) on 2024-05-30T19:55:03Z (GMT). No. of bitstreams: 2 license_rdf: 24251 bytes, checksum: 71ed42ef0a0b648670f707320be37b90 (MD5) MGFGA18.pdf: 2944072 bytes, checksum: d647e370a40a5a28360e7d377a1ab678 (MD5) Previous issue date: 2018application/pdfenengITISE 2018 : International conference on Time Series and Forecasting, Granada, Spain, 19-21 set. 2018Las 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 (CC - By 4.0)Anomaly detectionKalman filterHadoopReal time anomaly detection in network traffic time seriesPonenciainfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaMartínez Tagliafico, SergioGarcía González, GastónFernández, AliciaGómez, GabrielAcuña, JoséProcesamiento de SeñalesProcesamiento de SeñalesTelecomunicacionesTelecomunicacionesAnálisis de Redes, Tráfico y Estadísticas de ServiciosTratamiento de ImágenesAnálisis de Redes, Tráfico y Estadísticas de ServiciosTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/43953/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Real time anomaly detection in network traffic time series Martínez Tagliafico, Sergio Anomaly detection Kalman filter Hadoop |
| status_str | publishedVersion |
| title | Real time anomaly detection in network traffic time series |
| title_full | Real time anomaly detection in network traffic time series |
| title_fullStr | Real time anomaly detection in network traffic time series |
| title_full_unstemmed | Real time anomaly detection in network traffic time series |
| title_short | Real time anomaly detection in network traffic time series |
| title_sort | Real time anomaly detection in network traffic time series |
| topic | Anomaly detection Kalman filter Hadoop |
| url | https://hdl.handle.net/20.500.12008/43953 |
