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

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 th...

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Detalles Bibliográficos
Autores principales: Lopez-Martin, Manuel, Carro, Belen, Sanchez-Esguevillas, Antonio, Lloret, Jaime
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621014/
https://www.ncbi.nlm.nih.gov/pubmed/28846608
http://dx.doi.org/10.3390/s17091967
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author Lopez-Martin, Manuel
Carro, Belen
Sanchez-Esguevillas, Antonio
Lloret, Jaime
author_facet Lopez-Martin, Manuel
Carro, Belen
Sanchez-Esguevillas, Antonio
Lloret, Jaime
author_sort Lopez-Martin, Manuel
collection PubMed
description 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.
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spelling pubmed-56210142017-10-03 Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT Lopez-Martin, Manuel Carro, Belen Sanchez-Esguevillas, Antonio Lloret, Jaime Sensors (Basel) Article 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. MDPI 2017-08-26 /pmc/articles/PMC5621014/ /pubmed/28846608 http://dx.doi.org/10.3390/s17091967 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Lopez-Martin, Manuel
Carro, Belen
Sanchez-Esguevillas, Antonio
Lloret, Jaime
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
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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621014/
https://www.ncbi.nlm.nih.gov/pubmed/28846608
http://dx.doi.org/10.3390/s17091967
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