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Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks

Authors :
Jiaxing He
Xiaodan Wang
Yafei Song
Qian Xiang
Chen Chen
Source :
Applied Intelligence. 53:12416-12436
Publication Year :
2022
Publisher :
Springer Science and Business Media LLC, 2022.

Abstract

There is a class-imbalance problem that the number of minority class samples is significantly lower than that of majority class samples in common network traffic datasets. Class-imbalance phenomenon will affect the performance of the classifier and reduce the robustness of the classifier to detect unknown anomaly detection. And the distribution of the continuous features in the dataset does not follow the Gaussian distribution, which will bring great difficulties to intrusion detection. We propose Conditional Wasserstein Variational Autoencoders with Generative Adversarial Network (CWVAEGAN) to solve the class-imbalance phenomenon, CWVAEGAN transform the original dataset through data preprocessing, and then use the improved VAEGAN to generate minority class samples. According to the CWVAEGAN model, an intrusion detection system based on CWVAEGAN and One-dimensional convolutional neural networks (1DCNN), namely CWVAEGAN-1DCNN, is established. By using the examples generated by CWVAEGAN, the problem of intrusion detection on class unbalanced data is solved. Specifically, CWVAEGAN-1DCNN consists of three modules: data preprocessing module, CWVAEGAN, and deep neural network. We evaluate the performance of CWVAEGAN-1DCNN on two benchmark datasets and compared it with the other 16 methods. Experiment results suggest that the performance of CWVAEGAN-1DCNN is better than class-balancing methods, and other advanced methods.

Subjects

Subjects :
Artificial Intelligence

Details

ISSN :
15737497 and 0924669X
Volume :
53
Database :
OpenAIRE
Journal :
Applied Intelligence
Accession number :
edsair.doi...........0b9b2d65c1a57e4347159f0364e7bb5c