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。
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.
作者
谢燕
裴浪
XIE Yan;PEI Lang(School of Computer Science and Engineering,Hunan University of Information Technology,Changsha 410000,China;School of Computer Science,Wuhan Qingchuan University,Wuhan 430204,China)
出处
《吉林大学学报（信息科学版）》
CAS
2022年第2期240-246,共7页
Journal of Jilin University（Information Science Edition）
基金
湖南省教育厅科学研究重点基金资助项目(17A150)。
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
software defined networking(SDN)technology
data center network
abnormal traffic
traffic feature extraction
abnormal traffic detection
