Open AccessA Survey of techniques for fine-grained web traffic identification and classification
Author(s) -
Xiaolin Gui,
AUTHOR_ID,
Yuanlong Cao,
Ilsun You,
Lejun Ji,
Yong Luo,
Zhenzhen Luo,
AUTHOR_ID
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022138
Subject(s) - identification (biology) , computer science , traffic generation model , network traffic simulation , network traffic control , data science , malware , web traffic , computer network , computer security , world wide web , the internet , botany , network packet , biology
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.