Encrypted Network Traffic Classification using Self-supervised Learning

Abstract

Network traffic classification is used in many applications including network provisioning, malware detection, resource management, and so on. In modern networks, use of encrypted protocols is a norm rather than an exception. Existing network traffic classification techniques fall short in working with encrypted traffic. Although deep learning based techniques have been shown to perform well in the case of encrypted traffic classification, they require an abundance of labeled data to achieve high accuracy. However, labeled data is rarely available in sufficient volumes in real network settings as they require domain experts to annotate data with labels. Therefore, in this paper, we propose a self-supervised approach that can achieve high accuracy on encrypted network traffic classification with a few labeled data. The proposed method is evaluated on three publicly available datasets. The empirical result shows that our method not only achieves high accuracy on encrypted traffic but also has the ability to apply the acquired knowledge on a different dataset. In our experiments, our method outperforms the state-of-the-art baseline methods by ~3% in terms of accuracy even with a much lower volume of labeled data.