A Deep Learning Based Method for Handling Imbalanced Problem in Network Traffic Classification

Network traffic classification is an important problem in network traffic analysis. It plays a vital role in many network tasks including quality of service, firewall enforcement and security. One of the challenging problems of classifying network traffic is the imbalanced property of network data. Usually, the amount of traffic in some classes is much higher than the amount of traffic in other classes. In this paper, we proposed an application of a deep learning approach to address imbalanced data problem in network traffic classification. We used a recent proposed deep network for unsupervised learning called Auxiliary Classifier Generative Adversarial Network to generate synthesized data samples for balancing between the minor and the major classes. We tested our method on a well-known network traffic dataset and the results showed that our proposed method achieved better performance compared to a recent proposed method for handling imbalanced problem in network traffic classification.