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GCN-TC: Combining Trace Graph with Statistical Features for Network Traffic Classification

For machine-learning-based network traffic classification, we usually need large number of correctly labeled samples (ground truth) for model-training to get high accuracy. However in practical environments obtaining ground truth may be time-consuming and needs massive manual work, so achieving higher accuracy at low labeling rate is still a tricky problem. In this paper we propose a novel Graph Convolutional Network (GCN) based network traffic classification method named GCN-TC. We combine the traffic trace graph with statistical features in GCN model-training, so as to take advantage of both of them to achieve higher classification accuracy with very few labeled data. Experiment results show that the proposed model achieves the best performance with 13% higher in F1-score and 7.7% higher in accuracy than the best method of the rest.