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PAPER 3 Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT Manuel Lopez-Martin 1, Belen Carro 1,*, Antonio Sanchez-Esguevillas 1 and Jaime Lloret 2 1 Dpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain; manuel.lopezm@uva.es; belcar@tel.uva.es; antoniojavier.sanchez@uva.es 2 Instituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Camino Vera s/n, 46022 Valencia, Spain; jlloret@dcom.upv.es * Correspondence: belcar@tel.uva.es; Tel.: +34-983-423-980, Fax: +34-983-423-667 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. Keywords: intrusion detection; variational methods; conditional variational autoencoder; feature recovery; neural networks 1. Introduction A Network Intrusion Detection System (NIDS) is a system which detects intrusive, malicious activities or policy violations in a host or host’s network. The importance of NIDS is growing as the heterogeneity, volume and value of network data continue to increase. This is especially important for current Internet of Things (IoT) networks [1], which carry mission- critical data for business services. Intrusion detection systems can be host-based or network-based. The first monitor and analyze the internals of a computer system while the second deal with attacks on the communication interfaces [2]. For this work, we will focus on network-based systems. Intruders in a system can be internal or external. Internal intruders have access to the system but their privileges do not correspond to the access made, while the external intruders do not have access to the system. These intruders can perform a great variety of attacks: denial of service, probe, user to root attacks, etc. [2] Doctoral Thesis: Novel applications of Machine Learning to NTAP - 128PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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