TA-GAN: GAN based Traffic Augmentation for Imbalanced Network Traffic Classification

Abstract

As the mainstream in network traffic classification (NTC), machine learning (ML) based methods suffer performance degradation due to the imbalance distribution of Internet traffic. Data augmentation methods including the traditional oversampling techniques and the Generative Adversarial Network (GAN) based generation methods are most commonly used to counter the imbalance problem in NTC. However, the former is prone to overfitting and introducing noise. The latter overcomes the above weaknesses, but the quality of the generated traffic samples is difficult to judge. Besides, these methods all divide the imbalanced traffic classification problem into two subproblems, which cannot guarantee the global optimality. In this paper, we propose a GAN based Traffic Augmentation (TA-GAN) for imbalanced traffic classification. TA-GAN is an end-to-end framework that integrates the generation of the minority traffic samples with the training of the target classifier. We design the feedback mechanism to better guide the direction of the sample generation and simultaneously indicate the quality of the synthesized samples. Moreover, the existing deep learning-based NTC methods can be easily adapted to imbalance scenarios with TA-GAN. Comprehensive experiments on the public ISCXVPN2016 dataset demonstrate that TA-GAN effectively mitigates the influence of traffic imbalance (a maximum 14.64% improvement to the minority class' $F_{1}$ score) and outperforms the state-of-the-art methods.