Network traffic classification plays an important role in network monitoring and network management. Deep learning methods have become an effective traffic classification method due to its ability to automatically extract features. In the article, a two-branch spatio-temporal attention-based model is designed to represent bi-directional flows using multiple dimensional uniform packets as samples, 2DCNN with channel and spatial attention to extract spatial features, and LSTM with many-to-one attention mechanism to extract temporal features. The article conducts experiments on the public traffic dataset USTC-TFC2016. The results show that the classification performance of the model is better compared to the ablation experiments.

Qian Wang, Tianbo Xu. Research on Network Traffic Classification Method Based on Dual-branch Spatial-Temporal Attention. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 9: 1-10. https://doi.org/10.25236/AJCIS.2024.070901.

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