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

By Manuel López Martín, Belén Carro, Antonio J. Sánchez-Esguevillas, Jaime Lloret. 2017

267 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Control and Systems Engineering, Social Psychology

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Citations in the corpus

Deep Learning in Mobile and Wireless Networking: A Survey (2018, methodology): “They overview comprehensively existing efforts that incorporate deep learning into the IoT domain and shed light on current research challenges and future directions.”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “[148] used conditional variational autoencoder algorithm (CVAE) which is a generative model based on variational autoencoder (VAE) concepts.”
Adversarial Machine Learning for Network Intrusion Detection Systems: A Comprehensive Survey (2023, methodology): “…NIDS designed for IoT networks are evaluated with General network datasets [39], [48], [49], [53], and a large majority of recently proposed NIDS [39], [40], [41], [43], [46], [48], [49], [50] are evaluated with NSL-KDD and KDD Cup’99 only, which is over 20 years old and are not representative…”