TY - JOUR
T1 - Network traffic classification
T2 - Techniques, datasets, and challenges
AU - Azab, Ahmad
AU - Khasawneh, Mahmoud
AU - Alrabaee, Saed
AU - Choo, Kim Kwang Raymond
AU - Sarsour, Maysa
N1 - Publisher Copyright:
© 2023 Chongqing University of Posts and Telecommunications
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Deep learning
KW - Deep packet inspection
KW - Machine learning
KW - Network classification
KW - Traffic monitoring
UR - http://www.scopus.com/inward/record.url?scp=85146766916&partnerID=8YFLogxK
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U2 - 10.1016/j.dcan.2022.09.009
DO - 10.1016/j.dcan.2022.09.009
M3 - Review article
AN - SCOPUS:85146766916
SN - 2468-5925
JO - Digital Communications and Networks
JF - Digital Communications and Networks
ER -