An Encrypted Traffic Identification Method Based on RepVGG

With the emergence of encrypted traffic, more and more researchers use AI technology to improve the accuracy of traffic identification. However, machine learning needs to rely on human experience to extract features, and the training of deep learning models depends on a large number of labeled samples.To solve these problems, we propose an encrypted traffic identification method based on RepVGG. First, the pre-trained model RepVGG-A0 on the ImageNet dataset is migrated to the encrypted traffic dataset, and a dropout layer is added before the linear classifier in order to avoid overfitting. Then, to reduce the impact of sample imbalance, different weight parameters are assigned to different categories in the training process.Finally, we make a comparison with other traffic identification methods.The experimental results show that the proposed method can achieve 99.98% accuracy in binary classification and 97% accuracy in multi-classification experiments, which proves the effectiveness of the method.