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基于类均值向量及指数分布的流量行为特征分析 被引量：1

CLASS MEAN VECTOR AND EXPONENTIAL DISTRIBUTION TRAFFIC BEHAVIOR FEATURE ANALYSIS
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摘要 引入类均值向量度量及αβ指数分布方法,旨在提高分类正确率的基础上,克服由于抽样而带来的对分类结果的影响。利用流记录NOC_SET为DATASET,并以NETFLOW固有的测度和少量扩展测度为属性,利用所提出的FBRI(Flow behavior identification)属性选择算法对经典的机器学习算法进行流量识别。实验结果表明:任意比例的抽样对于采用FBRI属性选择的评估结果基本一致,并且利用FBRI属性选择算法可以提高应用识别正确率。 The introduction of class mean vector measurements and α β exponential distribution methods is to base on improving the correct classification ratio to overcome the classification result influence brought by sampling. Taking flow records NOC_SET as dataset, in addition taking NETFLOW＇s inherent measure and a few extended measures as features, the method uses the proposed FBRI （ Flow behavior identification） attribute selection algorithm to identify the traffic for classic machine learning algorithms. Experimental results show that the evaluation results to FBRI feature selections for arbitrary proportions of sampling are of the same ; additionally, by using FBRI feature selection algorithm proposed in the thesis, the correct identification ratio can be improved.
作者 董仕 丁伟
机构地区 东南大学计算机科学与工程学院 华东(北)地区网络中心
出处 《计算机应用与软件》 CSCD 2011年第11期249-253,共5页 Computer Applications and Software
关键词 类均值向量 αβ指数分布方法 NETFLOW 机器学习 Class mean vectors α β exponential distribution method NETFLOW Machine learning
分类号 TP3 [自动化与计算机技术—计算机科学与技术]
  • 相关文献

参考文献7

  • 1Karagiannis T, Papagiannaki K, Faloutsos M. BLINC : Multilevel traffic classification in the dark [ C ]//Proc. Of the ACM SIGCOMM. Philadelphia, 2005:229 -240.
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二级参考文献27

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共引文献63

同被引文献10

  • 1A W Moore, D Papagiannaki. Toward the Accurate Identification of Network Applications [C]// Proceedings of the Sixth Passive and Active Measurement Workshop (PAM 2005), March 2005.
  • 2Boston, USA: Springer, 2005: 41-54. P Haffner, S Sen, O Spatscheck, D Wang. ACAS: Automated Construction of Application Signatures [C]// SIGCOMM'05 MineNet Workshop, Philadelphia, USA, August 22-26, 2005. USA: ACM, 2005: 197-202.
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  • 6A W Moore, D Zuev. Intemet traffic classification using Bayesian analysis techniques [C]// Proc. of the 2005 ACM SIGMETRICS Int'l Conf. on Measurement and Modeling of Computer Systems, Banff, Canada, 2005. USA: ACM, 2008: 50-60.
  • 7L Wei, A W Moore. A Machine Learning Approach for Efficient Traffic Classification [C]// Proc. of the 15th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Istanbul, Turkey, 2007. USA: IEEE, 2007: 310-317.
  • 8L Yu, H Liu. Feature selection for high-dimensional data: a fast correlation-based filter solution [C]// Proc. of the Twentieth International Conference on Machine Learning (ICML), 2003. Washington DC, USA: AAAI Press, 2003: 856-863.
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  • 10A W Moore, D Zuev. Discriminators "for use in flow-based classification [R]// Technical Report, Intel Research, Cambridge, 2005. UK: Department of Computer Science~ Queen Mary, University of London, 2005: 1-14.

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计算机应用与软件
2011年 第11期

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