A Novel Approach for Semi-Supervised Network Traffic Classification

Abstract

Network traffic classification is the basic block for network management services such as network security and QoS control. When facing with the emergence of stronger encryption protocols like TLS 3.0, we need more advanced technologies, such as classical machine learning and deep learning methods to solve the problems. Every moment there are thousands of applications are sending the packets in the network, it's difficult for us to label all the sessions, we only have few labelled samples to train our classifiers. In this paper, we propose a semi supervised network traffic classification method. The basic idea is to use the samples segmented by encoder-decoder networks and unlabeled data to improve the performance of the classifier trained based on a small number of labeled samples. To explore the correlation of the continuously arriving packets, we segmented the sequence of packets' features parsed from raw traffic files (PCAPs), and using the segmentations to identify the traffic types. Then we adopt an Adversarial Auto-encoder to perform semi-supervised phase. The experimental study on VPN-nonVPN datasets shows that the proposed method is effective compared with the baseline method when there are relatively few available labeled training data.