一种基于综合特征的网络流量分类方法OA

With the rapid growth of Internet applications,traffic classification technology has become in-creasingly valuable in the fields of network management and security.Classification methods based on a single traffic feature can hardly meet the growing and complex requirements for traffic classification.Therefore,this paper proposes a novel network traffic classification approach based on comprehensive features,which utilizes a hybrid neural network to extract the global,local,and statistical features of traffic.The hybrid neural network combines a Transformer and a one-dimensional convolutional neural network(Transformer-1DCNN)in a mutually compensatory structure to extract both global and local fea-tures of traffic.Additionally,an autoencoder is employed to extract the statistical features of traffic.By fully exploiting the advantages of different features,this method enhances the accuracy and reliability of network traffic classification.Experimental results on the public dataset ISCX VPN-nonVPN2016 show that the proposed method achieves a classification accuracy of 98.3%,outperforming existing traffic clas-sification methods.