An encrypted traffic identification method based on multi-scale feature fusion

Abstract

As data privacy issues become more and more sensitive, increasing numbers of websites usually encrypt traffic when transmitting it. This method can largely protect privacy, but it also brings a huge challenge. Aiming at the problem that encrypted traffic classification makes it difficult to obtain a global optimal solution, this paper proposes an encrypted traffic identification model called the ET-BERT and 1D-CNN fusion network (BCFNet), based on multi-scale feature fusion. This method combines feature learning with classification tasks, unified into an end-to-end model. The local features of encrypted traffic extracted based on the improved Inception one-dimensional convolutional neural network structure are fused with the global features extracted by the ET-BERT model. The one-dimensional convolutional neural network is more suitable for the encrypted traffic of a one-dimensional sequence than the commonly used two-dimensional convolutional neural network. The proposed model can learn the nonlinear relationship between the input data and the expected label and obtain the global optimal solution with a greater probability. This paper verifies the ISCX VPN-nonVPN dataset and compares the results of the BCFNet model with the other five baseline models on accuracy, precision, recall, and F1 indicators. The experimental results demonstrate that the BCFNet model has a greater overall effect than the other five models. Its accuracy can reach 98.88%.

Keywords

• Encrypted traffic classification
• Global optimal solution
• Multi-scale feature fusion
• ET-BERT
• One-dimensional convolutional neural network