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Sensors 2017, 17(9), 1967; doi:10.3390/s17091967

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

1
Dpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
2
Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Camino Vera s/n, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Received: 16 July 2017 / Revised: 22 August 2017 / Accepted: 22 August 2017 / Published: 26 August 2017
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Abstract

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. View Full-Text
Keywords: intrusion detection; variational methods; conditional variational autoencoder; feature recovery; neural networks intrusion detection; variational methods; conditional variational autoencoder; feature recovery; neural networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Lopez-Martin, M.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT. Sensors 2017, 17, 1967.

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