Deep Learning-Based Encrypted Network Traffic Classification and Resource Allocation in SDN


  • Hao Wu School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
  • Xi Zhang School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
  • Jufeng Yang Signal and Communication Research Institute, China Academy of Railway Science Corporation, Beijing, China



Deep learning; Encrypted traffic; Fourier transform; Convolutional neural network; DFR architecture


In the rapid development of network technology, with the improvement of the quality and quantity of network users’ demands, more and more network information technology and excessive network traffic also raise people’s attention to the internal network security. Especially for the classification and resource allocation of encrypted network traffic, the research of related technologies has become the main research direction of the development of network technology. The extensive application of deep learning provides a new idea for the study of traffic classification. Therefore, on the basis of understanding the current situation, the improved convolutional neural network is selected to conduct an in-depth discussion on traffic classification and resource allocation of encrypted networks based on deep learning. The performance of the system is verified from the perspective of practical application.


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