Network traffic classification:Techniques,datasets,and challenges

Ahmad Azab ¹Mahmoud Khasawneh ²Saed Alrabaee ³Kim-Kwang Raymond Choo ⁴Maysa Sarsour⁵

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作者信息

1. College of Information Technology and Systems,Victorian Institute of Technology,Australia
2. College of Engineering,Al Ain University,Abu Dhabi,United Arab Emirates
3. Information Systems and Security,College of IT,United Arab Emirates University,Al Ain,15551,United Arab Emirates
4. Department of Information Systems and Cyber Security,University of Texas at San Antonio,San Antonio,TX,78260,USA
5. School of Photovoltaic and Renewable Energy Engineering,University of New South Wales,Sydney,NSW,2052,Australia
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Abstract

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.

Key words

Network classification/Machine learning/Deep learning/Deep packet inspection/Traffic monitoring

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出版年

2024

数字通信与网络(英文)

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