Survey of research on abnormal traffic detection for software defined networks

被引：0
|
作者：
Fu Y. [1 ]
Wang K. [1 ,2 ]
Duan X. [3 ,4 ]
Liu T. [1 ]
机构：
[1] Department of Information Security, Naval University of Engineering, Wuhan
[2] School of Mathematics and Information Engineering, Xinyang Vocational and Technical College, Xinyang
[3] College of Computer and Information Technology, Xinyang Normal University, Xinyang
[4] Henan Key Laboratory of Analysis and Applications of Education Big Data, Xinyang Normal University, Xinyang
来源：
Tongxin Xuebao/Journal on Communications | / 45卷 / 03期
基金：
国家重点研发计划;
关键词：
abnormal traffic detection; abnormal traffic mitigation; abnormal traffic traceability; deep learning; software defined network;
D O I：
10.11959/j.issn.1000-436x.2024016
中图分类号：
学科分类号：
摘要：
Since software defined network (SDN) was more vulnerable to network attacks than traditional networks, the research progress of abnormal traffic detection for software defined network in recent years from the technical principle and architecture characteristics was summarized, the possible organizational forms of network attacks on SDN were analyzed, and the characteristics, advantages, and disadvantages of current technologies related to abnormal traffic detection, abnormal traffic traceability, and abnormal traffic mitigation were discussed. The data sets commonly used in current research were compared and analyzed, and some general data preprocessing methods were sorted out. The research direction of abnormal traffic detection methods in the SDN environment in the future was summarized and prospected. The research results can guide the selection of adaptation methods in practical application requirements, and the problems and contradictions to be solved can guide subsequent research. © 2024 Editorial Board of Journal on Communications. All rights reserved.
引用
收藏
页码：208 / 226
页数：18
相关论文
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