Relational Graph Attention Network Combined with Burst Position Encoding for Traffic Classification

Siji Chen, Xi Xiao, Guangwu Hu, Le Yu, Qing Li, Hao Li, Qingjun Yuan, Dengpan Ye

Abstract: Network traffic classification has become an essential technology for information service providers. While existing methods predominantly focus on packet-level features such as port numbers and payload content, they fundamentally overlook the dynamic interaction patterns revealed by traffic burst sequences and the inherent relational characteristics between consecutive traffic bursts. To overcome the limitation of existing methods, we design a new burst position relational graph attention network (BP-RGAT) for traffic classification. We introduce the Heterogeneous Traffic Burst Graph (HTBG) to obtain more traffic interaction information. We also incorporate Relative Traffic Burst Position Encoding (RBPE) to capture sequence information between bursts. To evaluate the performance of BPRGAT, we conduct experiments with ISCX-VPN and USTC-TFC datasets. The results show that BP-RGAT achieves the highest F1 score compared to existing baseline methods (e.g. NetMamba, ET-BERT, BehavSniffer, TFE-GNN).

External IDs:dblp:conf/iwqos/ChenXHYLLYY25