A Survey of techniques for fine-grained web traffic identification and classificationopen access
- Authors
- Gui, Xiaolin; Cao, Yuanlong; You, Ilsun; Ji, Lejun; Luo, Yong; Luo, Zhenzhen
- Issue Date
- Jan-2022
- Publisher
- American Institute of Mathematical Sciences
- Keywords
- web traffic; traffic identification; traffic classification; fine-grained traffic identification
- Citation
- Mathematical Biosciences and Engineering, v.19, no.3, pp 2996 - 3021
- Pages
- 26
- Journal Title
- Mathematical Biosciences and Engineering
- Volume
- 19
- Number
- 3
- Start Page
- 2996
- End Page
- 3021
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/20335
- DOI
- 10.3934/mbe.2022138
- ISSN
- 1547-1063
1551-0018
- Abstract
- 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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