Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders
Resumen:
We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual learning framework, exploiting the generative properties of the underlying generative model to deal with continuously evolving data, avoiding catastrophic forgetting. We showcase the functioning of DC-VAE in the event of concept drifts, and propose the application of a novel approach to generative-driven continual learning, introducing the Deep Generative Replay model.
| 2022 | |
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Mathematics of computing Time series analysis Networks Network monitoring Variational Autoencoders Time-Series Anomaly Detection |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
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https://dl.acm.org/doi/10.1145/3517745.3563033#sec-terms
https://hdl.handle.net/20.500.12008/34457 |
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| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522899497582592 |
|---|---|
| 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, Austrian Institute of Technology Vienna, Austria 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 | 2022-10-31T16:33:13Z |
| dc.date.available.none.fl_str_mv | 2022-10-31T16:33:13Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual learning framework, exploiting the generative properties of the underlying generative model to deal with continuously evolving data, avoiding catastrophic forgetting. We showcase the functioning of DC-VAE in the event of concept drifts, and propose the application of a novel approach to generative-driven continual learning, introducing the Deep Generative Replay model. |
| 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 | 2 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. Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders [en línea]. Póster, 2022. |
| dc.identifier.doi.none.fl_str_mv | 10.1145/3517745.3563033 |
| dc.identifier.issn.none.fl_str_mv | 978-1-4503-9259-4 |
| dc.identifier.uri.none.fl_str_mv | https://dl.acm.org/doi/10.1145/3517745.3563033#sec-terms https://hdl.handle.net/20.500.12008/34457 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | ACM |
| dc.relation.ispartof.es.fl_str_mv | IMC 22 : Proceedings of the 22nd ACM Internet Measurement Conference, Nice, France, 25-27 oct. 2022, pp. 774-775. |
| 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 | Mathematics of computing Time series analysis Networks Network monitoring Variational Autoencoders Time-Series Anomaly Detection |
| dc.title.none.fl_str_mv | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| dc.type.es.fl_str_mv | Póster |
| 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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| id | COLIBRI_6c237c80179dd4e4d3f157443f4ae2e7 |
| identifier_str_mv | García González, G., Casas, P., Fernández, A. y otros. Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders [en línea]. Póster, 2022. 978-1-4503-9259-4 10.1145/3517745.3563033 |
| 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:20.500.12008/34457 |
| publishDate | 2022 |
| 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, Austrian Institute of Technology Vienna, AustriaFernández Alicia, Universidad de la República (Uruguay). Facultad de Ingeniería.Gómez Gabriel, Universidad de la República (Uruguay). Facultad de Ingeniería.2022-10-31T16:33:13Z2022-10-31T16:33:13Z2022García González, G., Casas, P., Fernández, A. y otros. Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders [en línea]. Póster, 2022.978-1-4503-9259-4https://dl.acm.org/doi/10.1145/3517745.3563033#sec-termshttps://hdl.handle.net/20.500.12008/3445710.1145/3517745.3563033Transferencia tecnológica. Grupo de investigación Detección de anomalías en series de tiempo, Facultad de Ingeniería. Instituto de Ingeniería Eléctrica.We present DC-VAE, an approach to network anomaly detection in multivariate time-series (MTS), using Variational Auto Encoders (VAEs) and Dilated Convolutional Neural Networks (CNN). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal and spatial MTS information. We showcase DC-VAE in different MTS datasets, and portray its future application in a continual learning framework, exploiting the generative properties of the underlying generative model to deal with continuously evolving data, avoiding catastrophic forgetting. We showcase the functioning of DC-VAE in the event of concept drifts, and propose the application of a novel approach to generative-driven continual learning, introducing the Deep Generative Replay model.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2022-10-27T16:20:55Z No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG22.pdf: 4053223 bytes, checksum: 41b76995ebded4a8fb142bdf4ff89fae (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2022-10-31T16:31:56Z (GMT) No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG22.pdf: 4053223 bytes, checksum: 41b76995ebded4a8fb142bdf4ff89fae (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2022-10-31T16:33:13Z (GMT). No. of bitstreams: 2 license_rdf: 23149 bytes, checksum: 1996b8461bc290aef6a27d78c67b6b52 (MD5) GCFG22.pdf: 4053223 bytes, checksum: 41b76995ebded4a8fb142bdf4ff89fae (MD5) Previous issue date: 20222 p.application/pdfenengACMIMC 22 : Proceedings of the 22nd ACM Internet Measurement Conference, Nice, France, 25-27 oct. 2022, pp. 774-775.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)Mathematics of computingTime series analysisNetworksNetwork monitoringVariational AutoencodersTime-SeriesAnomaly DetectionSteps towards continual learning in multivariate time-series anomaly detection using variational autoencodersPósterinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaGarcía González, GastónCasas, PedroFernández, AliciaGómez, GabrielProcesamiento de SeñalesTelecomunicacionesTratamiento de ImágenesTratamiento de ImágenesLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/34457/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; 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- Universidad de la Repúblicafalse |
| spellingShingle | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders García González, Gastón Mathematics of computing Time series analysis Networks Network monitoring Variational Autoencoders Time-Series Anomaly Detection |
| status_str | publishedVersion |
| title | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| title_full | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| title_fullStr | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| title_full_unstemmed | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| title_short | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| title_sort | Steps towards continual learning in multivariate time-series anomaly detection using variational autoencoders |
| topic | Mathematics of computing Time series analysis Networks Network monitoring Variational Autoencoders Time-Series Anomaly Detection |
| url | https://dl.acm.org/doi/10.1145/3517745.3563033#sec-terms https://hdl.handle.net/20.500.12008/34457 |
