TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for
  Fine-grained Encrypted Traffic Classification
Haozhen Zhang Le Yu Xi Xiao Qing Li Francesco Mercaldo Xiapu Luo Qixu Liu
Encrypted traffic classification is receiving widespread attention from
researchers and industrial companies. However, the existing methods only
extract flow-level features, failing to handle short flows because of
unreliable statistical properties, or treat the header and payload equally,
failing to mine the potential correlation between bytes. Therefore, in this
paper, we propose a byte-level traffic graph construction approach based on
point-wise mutual information (PMI), and a model named Temporal Fusion Encoder
using Graph Neural Networks (TFE-GNN) for feature extraction. In particular, we
design a dual embedding layer, a GNN-based traffic graph encoder as well as a
cross-gated feature fusion mechanism, which can first embed the header and
payload bytes separately and then fuses them together to obtain a stronger
feature representation. The experimental results on two real datasets
demonstrate that TFE-GNN outperforms multiple state-of-the-art methods in
fine-grained encrypted traffic classification tasks.