Encrypted traffic classification method based on spatio-temporal features adaptive fusion network
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.
cyber securityencrypted traffic classificationspatio-temporal features learningfusion strategy
国家科技期刊平台
NSTL
万方数据
维普
