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面向软件定义网络的异常流量检测研究综述OA北大核心CSTPCD

Survey of research on abnormal traffic detection for software defined networks

中文摘要英文摘要

针对软件定义网络(SDN)较传统网络更易遭受网络攻击的现实,从技术原理和架构特点出发,对近年来面向软件定义网络的异常流量检测研究进展进行综述,分析了 SDN 可能遭受网络攻击的组织形式,讨论了当前 SDN 异常流量检测、异常流量溯源、异常流量缓解相关技术的特点、优势及不足;对当前研究中常用的数据集进行了对比分析,并梳理出一些通用的数据预处理方法;总结并展望了未来 SDN 环境下异常流量检测方法的研究方向.调研结果可以指导实际应用需求中适配方法的选取,提出待解决的问题和矛盾可为后续研究提供引导.

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 ana-lyzed,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 re-search were compared and analyzed,and some general data preprocessing methods were sorted out.The research direc-tion 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.

付钰;王坤;段雪源;刘涛涛

海军工程大学信息安全系,湖北 武汉 430033海军工程大学信息安全系,湖北 武汉 430033||信阳职业技术学院数学与信息工程学院,河南 信阳 464000信阳师范大学计算机与信息技术学院,河南 信阳 464000||信阳师范大学河南省教育大数据分析与应用重点实验室,河南 信阳 464000

计算机与自动化

软件定义网络深度学习异常流量检测异常流量溯源异常流量缓解

software defined networkdeep learningabnormal traffic detectionabnormal traffic traceabilityabnormal traffic mitigation

《通信学报》 2024 (003)

208-226 / 19

国家重点研发计划基金资助项目(No.2018YFB0804104);基础加强计划技术领域基金资助项目(No.2021-JCJQ-JJ-0990) The National Key Research and Development Program of China(No.2018YFB0804104),Foundation Augmen-tation Program Technical Funding(No.2021-JCJQ-JJ-0990)

10.11959/j.issn.1000-436x.2024016

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