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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.00357 |
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| _version_ | 1866917179953250304 |
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| author | Zhou, Jiajun Sun, Changhui Shen, Meng Yu, Shanqing Xuan, Qi |
| author_facet | Zhou, Jiajun Sun, Changhui Shen, Meng Yu, Shanqing Xuan, Qi |
| contents | While pre-trained large models have achieved state-of-the-art performance in network traffic analysis, their prohibitive computational costs hinder deployment in real-time, throughput-sensitive network defense environments. This work bridges the gap between advanced representation learning and practical network protection by introducing Traffic-MoE, a sparse foundation model optimized for high-efficiency real-time inference. By dynamically routing traffic tokens to a small subset of specialized experts, Traffic-MoE effectively decouples model capacity from computational overhead. Extensive evaluations across three security-oriented tasks demonstrate that Traffic-MoE achieves up to a 12.38% improvement in detection performance compared to leading dense competitors. Crucially, it delivers a 91.62% increase in throughput, reduces inference latency by 47.81%, and cuts peak GPU memory consumption by 38.72%. Beyond efficiency, Traffic-MoE exhibits superior robustness against adversarial traffic shaping and maintains high detection efficacy in few-shot scenarios, establishing a new paradigm for scalable and resilient network traffic analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_00357 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Traffic-MoE: A Sparse Foundation Model for Network Traffic Analysis Zhou, Jiajun Sun, Changhui Shen, Meng Yu, Shanqing Xuan, Qi Cryptography and Security While pre-trained large models have achieved state-of-the-art performance in network traffic analysis, their prohibitive computational costs hinder deployment in real-time, throughput-sensitive network defense environments. This work bridges the gap between advanced representation learning and practical network protection by introducing Traffic-MoE, a sparse foundation model optimized for high-efficiency real-time inference. By dynamically routing traffic tokens to a small subset of specialized experts, Traffic-MoE effectively decouples model capacity from computational overhead. Extensive evaluations across three security-oriented tasks demonstrate that Traffic-MoE achieves up to a 12.38% improvement in detection performance compared to leading dense competitors. Crucially, it delivers a 91.62% increase in throughput, reduces inference latency by 47.81%, and cuts peak GPU memory consumption by 38.72%. Beyond efficiency, Traffic-MoE exhibits superior robustness against adversarial traffic shaping and maintains high detection efficacy in few-shot scenarios, establishing a new paradigm for scalable and resilient network traffic analysis. |
| title | Traffic-MoE: A Sparse Foundation Model for Network Traffic Analysis |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2601.00357 |