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López Martín, Manuel Carro Martínez, Belén Sánchez Esguevillas, Antonio Javier Lloret, Jaime 2022-11-09T12:43:49Z 2022-11-09T12:43:49Z 2017 Sensors, 2017, vol. 17, n. 9, p. 1967 https://uvadoc.uva.es/handle/10324/56882 10.3390/s17091967 1967 9 Sensors 17 1424-8220 Producción Científica 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. Ministerio de Economía y Competitividad y el Fondo de Desarrollo Regional (FEDER) dentro del proyecto “Distribución inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software”, Ref: TIN2014-57991-C3-1-P Ministerio de Economía y Competitividad y el Fondo de Desarrollo Regional (FEDER) dentro del proyecto “Inteligencia distribuida para el control y adaptación de redes dinámicas definidas por software", Ref: TIN2014-57991-C3-2-P application/pdf eng MDPI info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2017 The Author(s) Atribución 4.0 Internacional Intrusion detection Variational methods Conditional variational autoencoder Feature recovery Neural networks 33 Ciencias Tecnológicas Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in loT info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion https://www.mdpi.com/1424-8220/17/9/1967 SI