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Traffic identification model based on generative adversarial deep convolutional network

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Abstract

With the rapid development of network technology, the Internet has accelerated the generation of network traffic, which has made network security a top priority. In recent years, due to the limitations of deep packet inspection technology and port number-based network traffic identification technology, machine learning-based network traffic identification technology has gradually become the most concerned method in the field of traffic identification with its advantages. As the learning ability of deep learning in machine learning becomes more substantial and more able to adapt to highly complex tasks, deep learning has become more widely used in natural language processing, image identification, and computer vision. Therefore, more and more researchers are applying deep learning to network traffic identification and classification. To address the imbalance of current network traffic, we propose a traffic identification model based on generating adversarial deep convolutional networks (GADCN), which effectively fits and expands traffic images, maintains a balance between classes of the dataset, and enhances the dataset stability. We use the USTC-TFC2016 dataset as training and test samples, and experimental results show that the method based on GADCN has better performance than general deep learning models.

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Funding

This paper is supported by Project supported by Key Scientific and Technological Research Projects in Henan Province (Grand No. 192102210125), Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (KLNST-2020–2-01), Hubei Provincial Department of Education Youth Project (Q201316), and Hubei Provincial Department of Education Research Program Key Project (D20191708). In addition, the authors also will thank the anonymous reviewers for their comments and suggestions.

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Authors and Affiliations

  1. School of Computer and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei, China

    Shi Dong, Yuanjun Xia & Tao Peng

  2. School of Computer Science and Technology, Zhoukou Normal University, Zhoukou, Henan, China

    Shi Dong & Yuanjun Xia

  3. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications), Beijing, China

    Shi Dong

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  1. Shi Dong
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  2. Yuanjun Xia
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  3. Tao Peng
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Correspondence to Shi Dong or Tao Peng.

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Dong, S., Xia, Y. & Peng, T. Traffic identification model based on generative adversarial deep convolutional network. Ann. Telecommun. 77, 573–587 (2022). https://doi.org/10.1007/s12243-021-00876-6

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