吉林大学学报(信息科学版) ›› 2022, Vol. 40 ›› Issue (2): 240-246.

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基于 SDN 技术的数据中心网络异常流量检测算法

谢 燕1 , 裴 浪2   

  1. 1. 湖南信息学院 计算机科学与技术学院, 长沙 410000; 2. 武汉晴川学院 计算机学院, 武汉 430204
  • 收稿日期:2021-07-09 出版日期:2022-06-11 发布日期:2022-06-12
  • 作者简介:谢燕(1981— ), 女, 湖南湘潭人, 湖南信息学院讲师, 主要从事计算机网络研究, (Tel)86-18672198990(E-mail)whicu_xie@ sina. com; 裴浪(1983— ), 女, 湖南湘潭人, 武汉晴川学院副教授, 主要从事数据库应用、 数据分析和系统结构研究, (Tel)86-18672198990(E-mail)uyuy1616@ 163. com.
  • 基金资助:
    湖南省教育厅科学研究重点基金资助项目(17A150)

Research on Abnormal Traffic Detection Algorithm of Data Center Network Based on SDN Technology

XIE Yan 1 , PEI Lang 2   

  1. 1. School of Computer Science and Engineering, Hunan University of Information Technology, Changsha 410000, China; 2. School of Computer Science, Wuhan Qingchuan University, Wuhan 430204, China
  • Received:2021-07-09 Online:2022-06-11 Published:2022-06-12

摘要: 随着数据中心网络流量剧增, 导致异常流量攻击事件频繁发生, 严重威胁了用户数据安全, 为此, 提出 一种基于软件定义网络( SDN: Software-Defined Networking) 技术的数据中心网络异常流量检测算法。该算法 采用SDN 技术网络框架与时间、 频率集合方式构建数据流量传输流程, 利用模糊C均值聚类、 四元组、反向 传播(BP: Back Propagation)神经网络等算法提取数据流量特征, 利用主成分分析算法建立流量特征子空间, 并使用矩阵方式向子空间投影, 最后采用设定阈值和投影周期数据向量判断数据中心网络是否存在异常流量。实验结果表明, 所提算法不仅计算简便, 还能保证异常流量检测计算结果的精度, 有效维护数据中心网络稳定 与安全。

关键词: 软件定义网络(SDN)技术;  , 数据中心网络;  , 异常流量;  , 流量特征提取;  , 异常流量检测

Abstract: The sharp increase of data center network traffic leads to frequent abnormal traffic attacks, which seriously threatens the user data security. Therefore, a data center network abnormal traffic detection algorithm based on SDN( Software Defined Networking) technology is proposed. The data flow transmission process is constructed according to the SDN technology network framework and the time and frequency set method, and then the data flow characteristics are extracted using fuzzy C-means clustering, quadruple, BP(Back Propagation) neural network and other algorithms. The traffic feature subspace is established using the principal component analysis algorithm, and the matrix method is used to project to the subspace. Finally, the set threshold and projection period data vector are used to judge whether there is abnormal traffic in the data center network. The experimental results show that the proposed algorithm is an simple and ensures the accuracy of abnormal traffic detection results, and effectively maintains the stability and security of the data center network.

Key words: software defined networking ( SDN) technology;  , data center network;  , abnormal traffic;  , traffic feature extraction;  , abnormal traffic detection

中图分类号: 

  • TP393