Network Traffic Classification Method Based on Subspace Triple Attention Mechanism

Abstract

Network traffic classification plays an important role in network management. In order to improve classification accuracy of encrypted traffic, a method of encrypted network traffic classification based on subspace triple attention mechanism module is proposed. In this method, the network traffic data feature map is divided into several subspaces along the channel dimension. In each subspace, the one-dimensional feature coding calculation is carried out for the three channel branches respectively. ISCX public datasets, which including general and protocol encrypted network traffic data, is used for classification experiments. The results show that the proposed method can achieve better classification accuracy than other current methods on encrypted traffic datasets.