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Mathematical Biosciences and Engineering

2022, Volume 19, Issue 3: 2996-3021. doi: 10.3934/mbe.2022138
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A Survey of techniques for fine-grained web traffic identification and classification

  • 1.
    School of software, Jiangxi Normal University, Nanchang 330027, China
  • 2.
    Department of Information Security Engineering, Soonchunhyang University, Asan 31538, South Korea

  • Academic Editor: Thippa Reddy Gadekallu
  • Received: 30 September 2021 Revised: 17 December 2021 Accepted: 27 December 2021 Published: 17 January 2022

  • After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then, we conduct a detailed literature review on fine-grained network traffic identification from three perspectives: wired network, mobile network, and malware traffic identification. Finally, we also draw the conclusion on the challenges of fine-grained network traffic identification and future research prospects.

    Citation: Xiaolin Gui, Yuanlong Cao, Ilsun You, Lejun Ji, Yong Luo, Zhenzhen Luo. A Survey of techniques for fine-grained web traffic identification and classification[J]. Mathematical Biosciences and Engineering, 2022, 19(3): 2996-3021. doi: 10.3934/mbe.2022138

    Related Papers:

  • After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then, we conduct a detailed literature review on fine-grained network traffic identification from three perspectives: wired network, mobile network, and malware traffic identification. Finally, we also draw the conclusion on the challenges of fine-grained network traffic identification and future research prospects.


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