- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Lopez-Martin, Manuel es_ES
dc.contributor.author Carro, Belén es_ES
dc.contributor.author Sánchez-Esguevillas, Antonio es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2019-05-29T20:42:25Z
dc.date.available 2019-05-29T20:42:25Z
dc.date.issued 2017 es_ES
dc.identifier.uri http://hdl.handle.net/10251/121260
dc.description.abstract [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. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Intrusion detection es_ES
dc.subject Variational methods es_ES
dc.subject Conditional variational autoencoder es_ES
dc.subject Feature recovery es_ES
dc.subject Neural networks es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s17091967 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2014-57991-C3-1-P/ES/DISTRIBUCION INTELIGENTE DE SERVICIOS MULTIMEDIA UTILIZANDO REDES COGNITIVAS ADAPTATIVAS DEFINIDAS POR SOFTWARE/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Lopez-Martin, M.; Carro, B.; Sánchez-Esguevillas, A.; Lloret, J. (2017). Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT. Sensors. 17(9):1-17. https://doi.org/10.3390/s17091967 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://doi.org/10.3390/s17091967 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 17 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 17 es_ES
dc.description.issue 9 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 28846608 en_EN
dc.identifier.pmcid PMC5621014 en_EN
dc.relation.pasarela S\376344 es_ES
dc.contributor.funder Ministerio de Economía y Empresa es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem