文献类型：期刊文献

英文题名：Procedures, Criteria, and Machine Learning Techniques for Network Traffic Classification: A Survey

作者：Sheikh, Muhammad Sameer[1];Peng, Yinqiao[1]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2022

卷号：10

起止页码：61135

外文期刊名：IEEE ACCESS

收录：SCI-EXPANDED(收录号：WOS:000811541800001)、、EI(收录号：20222412226789)、Scopus(收录号：2-s2.0-85131758389)、WOS

基金：This work was supported in part by the Program for Scientific Research Start-Up Funds of Guangdong Ocean University under Grant E15046, in part by the Special Project on the Key Areas of New Generation of Information Technology under Grant 2020ZDZS3008, and in part by the Special Project of Artificial Intelligence under Grant 2019KZDZS1046.

语种：英文

外文关键词：Classification algorithms; Feature extraction; Intrusion detection; Telecommunication traffic; Internet; Peer-to-peer computing; Payloads; Classification criteria; machine learning method; obfuscation; security; traffic classification

外文摘要：Traffic classification is considered an important research area due to the increasing demand in network users. It not only effectively improve the network service identifications and security issues of the traffic network, but also provide robust accuracy and efficiency in different Internet application behaviors and patterns. Several traffic classification techniques have been proposed and applied successfully in recent years. However, the existing literature lack of comprehensive survey which could provide an overview and analysis towards the recent developments in network traffic classification. To this end, this survey presents a comprehensive investigation on traffic classification techniques by carefully reviewing existing methods from a new perspective. We comprehensively discuss the procedures and datasets for traffic classification. Additionally, traffic criteria are proposed, which could be beneficial to assess the effectiveness of the developed classification algorithm. Then, the traffic classification techniques are discussed in detail. Then, we thoroughly discussed the machine learning (ML) methods for traffic classification. For researcher's convenience, we present the traffic obfuscation techniques, which could be helpful for designing a better classifier. Finally, key findings and open research challenges for network traffic classification are identified along with recommendations for future research directions. In sum, this survey fills the gap of existing surveys and summarizes the latest research developments in traffic classification.