山西大学学报（自然科学版）2025，Vol.48Issue(3)：481-491,11.DOI:10.13451/j.sxu.ns.2023116
基于注意力的多特征融合加密流量识别方法
Attention-based Multi Feature Fusion Encrypted Traffic Recognition Method
摘要
Abstract
To address the issue of insufficient feature information extraction caused by neural network architecture in current encrypt-ed traffic recognition research,this paper proposes a multi-feature fusion encrypted traffic recognition method based on attention mechanism.The proposed method focuses on the hierarchical structure characteristics of encrypted traffic and designs two parallel network branches for feature extraction.Branch one uses residual neural network(ResNet)to extract the original features of traffic,while branch two uses an Inception-CNN composed of irregular-sized convolution kernels to extract statistical features of traffic for characterization and compensate for the information loss caused by traffic cropping.In addition,this paper converts the statistical features from the existing grayscale image to the RGBA image format as input to help the model more effectively extract features.The features extracted by the two branches are merged into a new feature vector and input into the channel attention module for weighting to enhance the representation ability of traffic features.The experimental results show that the proposed model performs better than existing typical encrypted traffic classification methods,with significantly improved accuracy,recall rate,and F1-score,among which the comprehensive performance metric F1-score is increased by an average of 6%compared to existing methods.关键词
加密流量/残差神经网络/特征融合/流量识别Key words
encrypted traffic/residual neural network/feature fusion/traffic identification分类
计算机与自动化引用本文复制引用
孙文茜,翟江涛,刘光杰,许成程..基于注意力的多特征融合加密流量识别方法[J].山西大学学报（自然科学版）,2025,48(3):481-491,11.基金项目
国家自然科学基金(61931004 （）
62072250) （）
国家重点研发计划(2021QY0700) （2021QY0700）
