A review of few-shot network traffic classification based on deep learning

Kai Yang; Ruo Nan Wang; Qi Meng; Long Meng; Zan Qi; Xue Li; Jin Long Fei

doi:10.1117/12.3006707

26 September 2023 A review of few-shot network traffic classification based on deep learning

Kai Yang, Ruo Nan Wang, Qi Meng, Long Meng, Zan Qi, Xue Li, Jin Long Fei

Proceedings Volume 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023); 127931H (2023) https://doi.org/10.1117/12.3006707
Event: International Conference on Mechatronics and Intelligent Control (ICMIC2023), 2023, Wuhan, China

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.

Citation Download Citation

Kai Yang, Ruo Nan Wang, Qi Meng, Long Meng, Zan Qi, Xue Li, and Jin Long Fei "A review of few-shot network traffic classification based on deep learning", Proc. SPIE 12793, International Conference on Mechatronics and Intelligent Control (ICMIC 2023), 127931H (26 September 2023); https://doi.org/10.1117/12.3006707

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KEYWORDS

Data modeling

Machine learning

Deep learning

Education and training

Statistical modeling

Gallium nitride

Performance modeling

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