Nethira: A Heterogeneity-aware Hierarchical Pre-trained Model for Network Traffic Classification
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911408854138880 |
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| author | Lin, Chungang Zhang, Weiyao Luo, Haitong Meng, Xuying Zhang, Yujun |
| author_facet | Lin, Chungang Zhang, Weiyao Luo, Haitong Meng, Xuying Zhang, Yujun |
| contents | Network traffic classification is vital for network security and management. The pre-training technology has shown promise by learning general traffic representations from raw byte sequences, thereby reducing reliance on labeled data. However, existing pre-trained models struggle with the gap between traffic heterogeneity (i.e., hierarchical traffic structures) and input homogeneity (i.e., flattened byte sequences). To address this gap, we propose Nethira, a heterogeneity-aware pre-trained model based on hierarchical reconstruction and augmentation. In pre-training, Nethira introduces hierarchical reconstruction at multiple levels-byte, protocol, and packet-capturing comprehensive traffic structural information. During fine-tuning, Nethira proposes a consistency-regularized strategy with hierarchical traffic augmentation to reduce label dependence. Experiments on four public datasets demonstrate that Nethira outperforms seven existing pre-trained models, achieving an average F1-score improvement of 9.11%, and reaching comparable performance with only 1% labeled data on high-heterogeneity network tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_22494 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nethira: A Heterogeneity-aware Hierarchical Pre-trained Model for Network Traffic Classification Lin, Chungang Zhang, Weiyao Luo, Haitong Meng, Xuying Zhang, Yujun Networking and Internet Architecture Network traffic classification is vital for network security and management. The pre-training technology has shown promise by learning general traffic representations from raw byte sequences, thereby reducing reliance on labeled data. However, existing pre-trained models struggle with the gap between traffic heterogeneity (i.e., hierarchical traffic structures) and input homogeneity (i.e., flattened byte sequences). To address this gap, we propose Nethira, a heterogeneity-aware pre-trained model based on hierarchical reconstruction and augmentation. In pre-training, Nethira introduces hierarchical reconstruction at multiple levels-byte, protocol, and packet-capturing comprehensive traffic structural information. During fine-tuning, Nethira proposes a consistency-regularized strategy with hierarchical traffic augmentation to reduce label dependence. Experiments on four public datasets demonstrate that Nethira outperforms seven existing pre-trained models, achieving an average F1-score improvement of 9.11%, and reaching comparable performance with only 1% labeled data on high-heterogeneity network tasks. |
| title | Nethira: A Heterogeneity-aware Hierarchical Pre-trained Model for Network Traffic Classification |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2601.22494 |