Accurate compressed traffic detection via traffic analysis using Graph Convolutional Network based on graph structure feature

Abstract

As the application of data compression technology expands in areas such as IoT, webpage, and video data transmission, there are problems such as leakage of compressed but unencrypted user data, difficulty in supervising compressed data, and confusion between compressed and private encrypted traffic. Existing compressed traffic detection methods are significantly affected by data length and rely on binary classification in one step using random tests. It remains a challenging task to conduct accurate and efficient compressed traffic detection. In this paper, we present GCN-RTG, a compressed traffic detection method using Graph Convolutional Network. We investigate the randomness feature transformation pattern of packet sequences, propose a graph structure based on this pattern, and design a powerful GCN-based classifier to detect compressed traffic. The experimental results show that GCN-RTG achieves 94% accuracy in compressed traffic detection, remarkably improving by nearly 10% accuracy compared with traditional machine learning methods and approximately 5% compared with CNN and LSTM. Considering the effect of private encrypted traffic, GCN-RTG attains an accuracy of 89% for detecting compressed traffic. Furthermore, GCN-RTG can maintain an 83% accuracy even in the most extreme packet loss scenario and reach an outstanding accuracy of up to 95% of compressed traffic detection in real-world network data sized 4GB from the Jiangsu education backbone network in China.