An Encrypted Traffic Classification Method Combining Graph Convolutional Network and Autoencoder
Boyu Sun, Wenyuan Yang, Mengqi Yan, Dehao Wu, Yuesheng Zhu, Zhiqiang Bai
Abstract
The increase in the source and size of encrypted network traffic brings significant challenges for network traffic analysis. The challenging problem in the encrypted traffic classification field is obtaining high classification accuracy with small number of labeled samples. To solve this problem, we propose a novel encryption traffic classification method that learns the feature representation from the traffic structure and the traffic flow data in this paper. We construct a K-Nearest Neighbor (KNN) traffic graph to represent the structure of traffic data, which contains more similarity information about the traffic. We utilize a two-layer Graph Convolutional Network (GCN) architecture for flows feature extraction and encrypted traffic classification. We further use the autoencoder to learn the representation of the flow data itself and integrate it into the GCN-learned representation to form a more complete feature representation. The proposed method leverages the benefits of the GCN and the autoencoder, which can obtain higher classification performance with only very few labeled data. The experimental results on two public datasets demonstrate that our method achieves impressive results compared to the state-of-the-art competitors.