RT info:eu-repo/semantics/article
T1 Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in loT
A1 López Martín, Manuel
A1 Carro Martínez, Belén
A1 Sánchez Esguevillas, Antonio Javier
A1 Lloret, Jaime
K1 Intrusion detection
K1 Variational methods
K1 Conditional variational autoencoder
K1 Feature recovery
K1 Neural networks
K1 33 Ciencias Tecnológicas
AB 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.
PB MDPI
YR 2017
FD 2017
LK https://uvadoc.uva.es/handle/10324/56882
UL https://uvadoc.uva.es/handle/10324/56882
LA eng
NO Sensors, 2017, vol. 17, n. 9, p. 1967
NO Producción Científica
DS UVaDOC
RD 17-jul-2024