Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

[EN] The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensi...

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Detalles Bibliográficos
Autores: Lopez-Martin, Manuel, Carro, Belén, Sánchez-Esguevillas, Antonio, Lloret, Jaime|||0000-0002-0862-0533
Tipo de recurso: artículo
Fecha de publicación:2017
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/121260
Acceso en línea:https://riunet.upv.es/handle/10251/121260
Access Level:acceso abierto
Palabra clave:Intrusion detection
Variational methods
Conditional variational autoencoder
Feature recovery
Neural networks
INGENIERIA TELEMATICA
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spelling Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoTLopez-Martin, ManuelCarro, BelénSánchez-Esguevillas, AntonioLloret, Jaime|||0000-0002-0862-0533Intrusion detectionVariational methodsConditional variational autoencoderFeature recoveryNeural networksINGENIERIA TELEMATICA[EN] The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.This work has been partially funded by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional (FEDER) within the project "Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software, Ref: TIN2014-57991-C3-2-P", and the Project "Distribucion inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software", Ref: TIN2014-57991-C3-1-P, in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento.MDPI AGDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaMinisterio de Economía y EmpresaRepositorio Institucional de la Universitat Politècnica de València Riunet20172017-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/121260reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengMinisterio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2014-57991-C3-1-P DISTRIBUCION INTELIGENTE DE SERVICIOS MULTIMEDIA UTILIZANDO REDES COGNITIVAS ADAPTATIVAS DEFINIDAS POR SOFTWAREopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1212602026-06-13T07:49:27Z
dc.title.none.fl_str_mv Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
title Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
spellingShingle Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
Lopez-Martin, Manuel
Intrusion detection
Variational methods
Conditional variational autoencoder
Feature recovery
Neural networks
INGENIERIA TELEMATICA
title_short Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
title_full Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
title_fullStr Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
title_full_unstemmed Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
title_sort Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
dc.creator.none.fl_str_mv Lopez-Martin, Manuel
Carro, Belén
Sánchez-Esguevillas, Antonio
Lloret, Jaime|||0000-0002-0862-0533
author Lopez-Martin, Manuel
author_facet Lopez-Martin, Manuel
Carro, Belén
Sánchez-Esguevillas, Antonio
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Carro, Belén
Sánchez-Esguevillas, Antonio
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Ministerio de Economía y Empresa
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Intrusion detection
Variational methods
Conditional variational autoencoder
Feature recovery
Neural networks
INGENIERIA TELEMATICA
topic Intrusion detection
Variational methods
Conditional variational autoencoder
Feature recovery
Neural networks
INGENIERIA TELEMATICA
description [EN] The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.
publishDate 2017
dc.date.none.fl_str_mv 2017
2017-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/121260
url https://riunet.upv.es/handle/10251/121260
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Ministerio de Economía y Competitividad http://dx.doi.org/10.13039/501100003329 TIN2014-57991-C3-1-P DISTRIBUCION INTELIGENTE DE SERVICIOS MULTIMEDIA UTILIZANDO REDES COGNITIVAS ADAPTATIVAS DEFINIDAS POR SOFTWARE
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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