FineNet: Few-Shot Mobile Encrypted Traffic Classification via a Deep Triplet Learning Network Based on Transformer

Shengbao Li, Qian Qiang, Tianning Zang, Lanqi Yang, Tianye Gao

Abstract: The existing encrypted mobile traffic classification methods often require a large number of training sets to ensure the effect of the model. When it is difficult to collect enough samples, these methods may not yield satisfactory results. Therefore, it is crucial to study an effective few-shot learning method to achieve accurate traffic classification with very few samples. In this paper, we propose FineNet, a few-shot fine-grained classification technology based on a triplet deep learning network with Transformer. The triplet deep learning network is able to discover subtle differences between traffic flows and effectively transfer existing classification knowledge to few-shot scenarios. Moreover, we introduce Transformer as the base model for the triplet network, fully leveraging Transformer's powerful representation capabilities for sequential data to enhance the express ability of FineNet. We conduct multiple comparative experiments, and the result proves that the accuracy rate is at least 2.4% higher than state-of-the-art approaches in few-shot environment.

External IDs:dblp:conf/wcnc/LiQZYG24