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- W4387060341 abstract "At present, deep learning has achieved excellent performance in the field of network traffic classification. However, deep learning relies on massive data-driven classification models. When the data set is small, it is usually hindered. In order to solve this problem, few-shot network traffic classification technology based on deep learning has been gradually studied. In this paper, we conducted a comprehensive survey to fully understand the few-shot traffic classification techniques. We first define the few-shot traffic classification problem. Based on how to solve the contradiction between the few-shot data set and the large number of parameters to be trained in the model, we classify the current few-shot traffic classification method based on deep learning from two perspectives: (1) Data augmentation method, which uses the method of expanding the data set to enhance the supervision experience. In this paper, the methods based on data enhancement are divided into two categories based on GAN and feature transformation.(2) Model-based methods. Model-based methods are divided into three categories: transfer learning, metric learning and meta-learning. And discuss the advantages and disadvantages of each classification method. Finally, the results are summarized and the future development direction of few-shot network traffic classification technology based on deep learning is prospected." @default.
- W4387060341 created "2023-09-27" @default.
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- W4387060341 date "2023-09-26" @default.
- W4387060341 modified "2023-10-10" @default.
- W4387060341 title "A review of few-shot network traffic classification based on deep learning" @default.
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- W4387060341 doi "https://doi.org/10.1117/12.3006707" @default.
- W4387060341 hasPublicationYear "2023" @default.
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