BiST-Mamba: A Dual-branch Spatio-Temporal Mamba Network for Encrypted Traffic Classification (Student Abstract)

Abstract

Encrypted traffic classification has become increasingly important in network security. To address the difficulty of existing architectures in collaboratively modeling spatio-temporal features, we propose BiST-Mamba, a novel dual-branch spatio-temporal Mamba network that enables simultaneous representation of spatio-temporal features. To the best of our knowledge, this is the first work to introduce VMamba into encrypted traffic classification. Preliminary experiments on a small-scale dataset show that our accuracy and F1 scores reach 94.13% and 93.41%, respectively. The method achieves promising classification performance, demonstrating the potential of the model for effective spatio-temporal modeling.