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TFE-GNN: A Temporal Fusion Encoder Using Graph Neural Networks for Fine-grained Encrypted Traffic Classification

arXiv 2307.16713 ↗First posted 31 Jul 2023cs.LG cs.AIcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 18 of its 48 references that are in the map. First posted on arXiv 31 Jul 2023.

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18 of 48 references.

References · 18

  1. 2017Attention Is All You Needcited by 59,932
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6 more
  1. 2020Design Space for Graph Neural Networkscited by 182
  2. 2017Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learningcited by 69
  3. 2022ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classificationcited by 66
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 4; 2026: 5.

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  1. 2024NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mambacited by 24
  2. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  3. 2025Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Modelcited by 2
  4. 2024One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learningcited by 1
  5. 2024Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classificationcited by 1
  6. 2026NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classificationcited by 1
  7. 2026UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shiftscited by 1
  8. 2026SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarkscited by 1
  9. 2025PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoningcited by 0
  10. 2025Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networkscited by 0
  11. 2025Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Frameworkcited by 0
  12. 2026MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretrainingcited by 0
1 more
  1. 2026PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classificationcited by 0