中南民族大学学报（自然科学版）2024，Vol.43Issue(3)：384-392,9.DOI:10.20056/j.cnki.ZNMDZK.20240313
基于时空特征自适应融合网络的加密流量分类方法
Encrypted traffic classification method based on spatio-temporal features adaptive fusion network
摘要
Abstract
The data packets of encrypted traffic have obvious temporal features.The existing methods are difficult to extract the hidden temporal features in the traffic data.The temporal features and spatial features cannot be effectively integrated,and most of the open datasets have the problem of sample imbalance between classes which brings great challenges to the accurate classification of encrypted traffic.For the above problems,a Convolutional Neural Network model including a spatio-temporal feature extraction module and a hard samples learning module is proposed.Firstly convolution kernels of different dimensions in the spatio-temporal feature extraction module are used to synchronously learn the temporal and spatial features in the traffic data packet sequence.Then the extracted spatio-temporal features are effectively fused by using the adaptive weighted fusion strategy.The hard sample learning module uses the focus function to make the model more inclined to learned hard samples during the training process,which further balances the classification effects of different classes.The experimental results show that the classification accuracy rates of the above method on the ISCX VPN-nonVPN2016 dataset and the USTC-TFC2016 dataset are 99.38%and 99.46%respectively,and the F1 score for the classification results of different traffic classes are 99.04%and 99.31%,which has better recognition performance compared with the current similar methods.关键词
网络安全/加密流量分类/时空特征学习/融合策略Key words
cyber security/encrypted traffic classification/spatio-temporal features learning/fusion strategy分类
信息技术与安全科学引用本文复制引用
陈拓,石浩,李翔杰,吴能光..基于时空特征自适应融合网络的加密流量分类方法[J].中南民族大学学报（自然科学版）,2024,43(3):384-392,9.基金项目
浙江省医药卫生科技计划资助项目(2022PY037) （2022PY037）
