An Accurate and Extensible Machine Learning Classifier for Flow-Level Traffic Classification

Machine Learning (ML) techniques have been widely applied in recent traffic classification. However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset, we apply SMOTE algorithm in generating artificial training samples for minority classes. We evaluate our classifier on two datasets collected from different network border routers. Compared with the previous multi-class traffic classifiers built in one-time training process, our classifier achieves much higher F-Measure and AUC for each application.