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Network traffic classification:Techniques,datasets,and challenges

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In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

Network classificationMachine learningDeep learningDeep packet inspectionTraffic monitoring

Ahmad Azab、Mahmoud Khasawneh、Saed Alrabaee、Kim-Kwang Raymond Choo、Maysa Sarsour

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College of Information Technology and Systems,Victorian Institute of Technology,Australia

College of Engineering,Al Ain University,Abu Dhabi,United Arab Emirates

Information Systems and Security,College of IT,United Arab Emirates University,Al Ain,15551,United Arab Emirates

Department of Information Systems and Cyber Security,University of Texas at San Antonio,San Antonio,TX,78260,USA

School of Photovoltaic and Renewable Energy Engineering,University of New South Wales,Sydney,NSW,2052,Australia

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2024

10.1016/j.dcan.2022.09.009

数字通信与网络(英文)

数字通信与网络(英文)

ISSN：
年,卷(期)：2024.10(3)