ENCRYPTED TRAFFIC CLASSIFICATION METHOD BASED ON DENSELY CONNECTED CONVOLUTIONAL NETWORK
Aimed at the problems that traditional methods rely on manual feature extraction and the existing deep learning-based methods are not high enough for multi-classification,a densely connected convolutional network-based encryption traffic classification method is proposed.The densely connected structure of each layer of convolution in DenseBlock was used to reduce the disappearance of gradients and enhance feature transfer to improve classification performance.Experiments were conducted on the public data set"ISCX VPN-nonVPN".The results show that this method has a better classification effect on different types of encrypted traffic,with an accuracy of 98.56％and an F1 Score of 98.55％.Compared with the methods based on the 1D-CNN and the ResNet model,the accuracy is increased by 8.88 and 6.54 percentage points,and the F1 Score is increased by 8.86 and 6.64 percentage points,respectively.
万方数据
维普
