计算机科学 ›› 2013, Vol. 40 ›› Issue (11): 48-51.

• 网络与通信 • 上一篇    下一篇

基于递归率REC特征的网络流量相空间重构监测

谢胜军,殷锋,周绪川   

  1. 西南民族大学网络中心 成都610041;西南民族大学网络中心 成都610041;西南民族大学计算机科学与技术学院 成都610041
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受中央高校基本科研业务费专项基金项目(12NZYQN27)资助

Testing of Network Traffic Series in Reconstructed Phase Space Based on Recurrence Rate Feature

XIE Sheng-jun,YIN Feng and ZHOU Xu-chuan   

  • Online:2018-11-16 Published:2018-11-16

摘要: 传统上对网络流时间序列分析多采用线性分析方法,没有充分利用到网络流客观存在的非线性特征信息,从而使数据分析能力受限。提出了基于定量递归分析递归率REC特征的网络流量相空间重构监测模型,基于相空间重构和递归图分析,设计了网络流量的REC递归率的定量递归特征作为网络流量序列分析的数据支撑。使用平均互信息算法和虚假最近邻点算法求取流量序列的相空间重构的关键参数,利用递归图中有规律的点线检验网络总出口流量的确定性和可预测性,利用REC特征监测网络流量序列的异常流量和特性进行分析。仿真实验表明,网络流量序列的定量递归特征具有较强的稳定性和自相似性,精度较传统特征统计方法提高19%以上,采用REC递归率特征对异常流量序列的预测预报监测准确率为99.7%,比采用传统的其它非线性递归特征提高了13.2%,展示了算法在网络流量和非平稳数据序列分析中的优越性能。

关键词: 网络流量,递归率,相空间重构,递归图

Abstract: Traditional linear analysis method takes the network traffic time series analysis,and does not fully use the exi-sted non-linear feature information.The data analysis performance is limited as result.An improved testing model of network traffic with phase space reconstruction based on recurrence quantitative analysis(RQA)was proposed,and on the basis of phase space reconstruction,the recurrence rate called REC feature was designed as the sequence data support.The average mutual information algorithm and false nearest neighbors algorithm were proposed in calculating the key parameters for phase space reconstruction.The determinism and prediction property of network traffic were tested based on the regular points and lines,and the abnormal flow feature was detected based on REC recurrence feature.Simu-lation result shows that the series has strong stability and self-similar characteristics,and the feature detection performance is stable and precise,and the monitoring precision is improved by 19% with REC feature,and the abnormal traffic detection precision is 99.7% with the improvement of 13.2%.It shows good performance in analysis of network traffic and non-stationary data sequence.

Key words: Network traffic,Recurrence rate,Phase space reconstruction,Recurrence plot

[1] 闫源江,胡光波,周勇.舰船辐射噪声的非线性和确定性检验[J].舰船电子工程,2010,0(10):150-153
[2] Stan C,Cristescu C P,Dimitriu D G.Analysis of the intermittent behavior in a low-temperature discharge plasma by recurrence plot quantification[J].Physics of Plasmas,2010,17(4):1-6
[3] lan T P,James W S,Sadasivam S,et al.Attractor structure discriminates sleep states:Recurrence plot analysis applied to infant breathing patterns [J].IEEE Transactions on Biomedical Engineering,2010,57(5):1108-1116
[4] 朱凡,吴敏.基于定量递归分析的校园网流量特性分析[J].计算机应用与软件,2012,9(6):275-281
[5] 李超顺,周建中,方仍存,等.基于混沌优化的模糊聚类分析方法[J].系统仿真学报,2009,21(10):2977-2980
[6] 汪中才,黎永碧.基于数据挖掘的入侵检测系统研究[J].科技通报,2012,28(8):150-152
[7] Kousik G,Basabi B,Roy C A.Using recurrence plot analysis to distinguish between endogenous and exogenous stock market crashes[J].Physica A:Statistical Mechanics and its Applications,2010,389(9):1874-1882
[8] 赵岩,何鹏.网络流量的非线性组合预测模型应用研究[J].计算机仿真,2012,6(29):140-144
[9] 张宾,杨家海,吴建平.Internet 流量模型分析与评述[J].软件学报,2011,2(1):115-131
[10] 丁玲,赵小刚.改进BP神经网络用于入侵检测[J].微计算机信息,2012,28(3):131-134

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