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.
@article{arxiv.2508.19924,
title = {FlowletFormer: Network Behavioral Semantic Aware Pre-training Model for Traffic Classification},
author = {Liming Liu and Ruoyu Li and Qing Li and Meijia Hou and Yong Jiang and Mingwei Xu},
journal= {arXiv preprint arXiv:2508.19924},
year = {2025}
}