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

arXiv: https://arxiv.org/abs/2307.16713 · HTML: https://mlsanity.com/paper/2307.16713/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 48 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Representation Learning on Graphs with Jumping Knowledge Networks](https://mlsanity.com/paper/1806.03536/) (2018)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Distribution Alignment: A Unified Framework for Long-tail Visual Recognition](https://mlsanity.com/paper/2103.16370/) (2021)
- [Design Space for Graph Neural Networks](https://mlsanity.com/paper/2011.08843/) (2020)
- [Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning](https://mlsanity.com/paper/1709.02656/) (2017)
- [ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification](https://mlsanity.com/paper/2202.06335/) (2022)
- [On Provable Benefits of Depth in Training Graph Convolutional Networks](https://mlsanity.com/paper/2110.15174/) (2021)
- [Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning](https://mlsanity.com/paper/1801.02265/) (2018)
- [k-fingerprinting: a Robust Scalable Website Fingerprinting Technique](https://mlsanity.com/paper/1509.00789/) (2015)

## Built on by (most cited first, 13 of 13)

- [NetMamba: Efficient Network Traffic Classification via Pre-training Unidirectional Mamba](https://mlsanity.com/paper/2405.11449/) (2024)
- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model](https://mlsanity.com/paper/2501.03279/) (2025)
- [One Train for Two Tasks: An Encrypted Traffic Classification Framework Using Supervised Contrastive Learning](https://mlsanity.com/paper/2402.07501/) (2024)
- [Efficiently and Effectively: A Two-stage Approach to Balance Plaintext and Encrypted Text for Traffic Classification](https://mlsanity.com/paper/2407.19687/) (2024)
- [NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classification](https://mlsanity.com/paper/2601.21792/) (2026)
- [UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts](https://mlsanity.com/paper/2605.17575/) (2026)
- [SoK: Where Do Flow Labels Come From? Auditing Label Provenance in Encrypted Traffic Benchmarks](https://mlsanity.com/paper/2609.02140/) (2026)
- [PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning](https://mlsanity.com/paper/2503.03747/) (2025)
- [Learning Dynamic Graphs via Tensorized and Lightweight Graph Convolutional Networks](https://mlsanity.com/paper/2504.15613/) (2025)
- [Building Transparency in Deep Learning-Powered Network Traffic Classification: A Traffic-Explainer Framework](https://mlsanity.com/paper/2509.18007/) (2025)
- [MambaNetBurst: Direct Byte-level Network Traffic Classification without Tokenization or Pretraining](https://mlsanity.com/paper/2605.11034/) (2026)
- [PERO: Efficient Robust Post-Training Foundation Models for Encrypted Traffic Classification](https://mlsanity.com/paper/2608.15504/) (2026)

## 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.