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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.19924 |
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| _version_ | 1866914009163235328 |
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| author | Liu, Liming Li, Ruoyu Li, Qing Hou, Meijia Jiang, Yong Xu, Mingwei |
| author_facet | Liu, Liming Li, Ruoyu Li, Qing Hou, Meijia Jiang, Yong Xu, Mingwei |
| contents | Network traffic classification using pre-training models has shown promising results, but existing methods struggle to capture packet structural characteristics, flow-level behaviors, hierarchical protocol semantics, and inter-packet contextual relationships. To address these challenges, we propose FlowletFormer, a BERT-based pre-training model specifically designed for network traffic analysis. FlowletFormer introduces a Coherent Behavior-Aware Traffic Representation Model for segmenting traffic into semantically meaningful units, a Protocol Stack Alignment-Based Embedding Layer to capture multilayer protocol semantics, and Field-Specific and Context-Aware Pretraining Tasks to enhance both inter-packet and inter-flow learning. Experimental results demonstrate that FlowletFormer significantly outperforms existing methods in the effectiveness of traffic representation, classification accuracy, and few-shot learning capability. Moreover, by effectively integrating domain-specific network knowledge, FlowletFormer shows better comprehension of the principles of network transmission (e.g., stateful connections of TCP), providing a more robust and trustworthy framework for traffic analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_19924 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification Liu, Liming Li, Ruoyu Li, Qing Hou, Meijia Jiang, Yong Xu, Mingwei Machine Learning Network traffic classification using pre-training models has shown promising results, but existing methods struggle to capture packet structural characteristics, flow-level behaviors, hierarchical protocol semantics, and inter-packet contextual relationships. To address these challenges, we propose FlowletFormer, a BERT-based pre-training model specifically designed for network traffic analysis. FlowletFormer introduces a Coherent Behavior-Aware Traffic Representation Model for segmenting traffic into semantically meaningful units, a Protocol Stack Alignment-Based Embedding Layer to capture multilayer protocol semantics, and Field-Specific and Context-Aware Pretraining Tasks to enhance both inter-packet and inter-flow learning. Experimental results demonstrate that FlowletFormer significantly outperforms existing methods in the effectiveness of traffic representation, classification accuracy, and few-shot learning capability. Moreover, by effectively integrating domain-specific network knowledge, FlowletFormer shows better comprehension of the principles of network transmission (e.g., stateful connections of TCP), providing a more robust and trustworthy framework for traffic analysis. |
| title | FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.19924 |