Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

A Survey of techniques for fine-grained web traffic identification and classificationopen access

Authors
Gui, XiaolinCao, YuanlongYou, IlsunJi, LejunLuo, YongLuo, 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.
Files in This Item
There are no files associated with this item.
Appears in
Collections
ETC > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE