FA-Net: More Accurate Encrypted Network Traffic Classification Based on Burst with Self-Attention
Minghao Jiang, Mingxin Cui, Chengshang Hou, Wei Cai, Zhen Li, Gang Xiong, Gaopeng Gou · 2023
Encrypted network traffic classification (ENTC) is crucial in fields including network cyberspace security, network administration and service quality. Combining the machine learning algorithms with manual-designed burst features has been studied extensively in the ENTC community. However, these features depend on professional experience heavily, which needs lots of human effort. These hand-crafted features are task-oriented and incomplete in various complex tasks. What's more, they are also affected by the potential network jitters. In this paper, we propose a novel encrypted traffic classification method FA-Net to mine burst features. We adopt two hierarchical multi-head self-attention encoders to enumerate all potential intra-burst features and inter-burst dependencies completely, and select the optimal associations automatically. For more robust against network jitter, we design an additional burst positional encoding to loose the model's sensitivity about out-of-order packets within bursts. We evaluate the FA-Net on multiple datasets, including website and mobile application classification tasks. The results show the FA-Net model outperforms other state-of-the-art methods in all the datasets, even gains more than 5% absolute improvement in accuracy. Additionally, the quantitative measurements about burst feature similarity show that the burst features learned by FA-Net exhibits more intraclass similarity and more inter-class separation.