Combating Imbalance in Network Traffic Classification Using GAN Based Oversampling

Abstract

With the proliferation of encrypted traffic, machine learning (ML) based network traffic classification (NTC) has become the mainstream method. However, most studies ignored two issues. On the one hand, Internet traffic presents a natural uneven distribution. On the other hand, machine learning algorithms generally aim to achieve the highest overall accuracy without considering class imbalance. This leads to severe performance degradation of existing ML-based NTC schemes when facing imbalanced scenarios. In this paper, we design a novel Generative Adversarial Network (GAN) architecture to generate traffic samples, in which the addition of the classifier and the pretraining module makes the generation process more stable and effective. We propose an end-to-end framework for imbalanced traffic classification, named ITCGAN, which can generate traffic samples for minority classes to adaptively rebalance the original traffic and simultaneously train the optimal classifier. We evaluate its effectiveness on the public ISCXVPN2016 dataset based on the global metrics and individual metrics. The results show that our method performs well in imbalanced NTC tasks, fully alleviating the performance degradation (a 10.27-percentage-point improvement to the precision of the most minority class). Meanwhile, it surpasses five state-of-the-art oversampling methods.