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基于决策树的网络流量分类方法 被引量：7

Traffic Classification Based on Decision Tree
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摘要 针对传统流量分类方法(基于端口和有效载荷)分类不可靠的问题,提出基于C4.5决策树算法,根据训练集中属性的信息增益比率构建分类模型,按属性对测试数据集进行预测,通过查找分类模型实现对网络流量的分类。在公开数据集和自己采集的数据集上进行实验,结果表明,采用C4.5决策树算法对网络流量分类,平均分类精度为93%,单类别分类精度均在90%以上,能有效地实现对网络流量应用类型的识别。 Aiming at the problem of instability in traditional traffic classification methods, a traffic classification method based on CA. 5 decision tree is proposed, which establishes models on the information gain ratio from the training set. Classifier is tested by attrib- utes on test dataset, as well as network traffic is classified by searching classification models. Experiments show that the overall accuracy of our method achieves more than 93% ,and the accuracy of single class is more than 90% on open dataset. So the method is effective for classifying various kinds of traffic.
作者 于孝美 陈贞翔 彭立志
机构地区 山东省网络环境智能计算技术重点实验室 济南大学信息科学与工程学院
出处 《济南大学学报（自然科学版）》 CAS 北大核心 2012年第3期291-295,共5页 Journal of University of Jinan(Science and Technology)
基金 国家自然科学基金(60903176) 山东省中青年科学家奖励基金(BS2009DX037)
关键词 流量分类 决策树 网络流 统计属性 traffic classification decision tree network flow statistic attribute
分类号 TP393.0 [自动化与计算机技术—计算机应用技术]
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参考文献19

  • 1CRISTIANO C, LUCA C. Optimization of a peer-to-peer system for efficient content [ J ]. European Journal of Operational Research, 2009,196(2) :423 -433.
  • 2YANG Bo, WANG Lin, CHEN Zhenxiang, et al. A novel classification method using the combination of FDPS and flexible neural tree [ J ]. Neurocomputing, 2010,73 ( 4/6 ) : 690 - 699.
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二级参考文献29

  • 1Madhukar A, Williamson C. A longitudinal study of P2P traffic classification [C]//Proc of the 14th IEEE Int Syrup on Modeling, Analysis, and Simulation. Washington, DC IEEE Computer Society, 2006:179-188
  • 2Moore A W, Papagiannaki K. Toward the accurate identification of network applications [G]//Dovrolis C. LNCS 3431: Proc of the PAM 2005. Heidelberg: Springer, 2005:41-54
  • 3Karagiannis T, Papagiannaki K, Faloutsos M. BLINC: Multilevel traffic classification in the dark [C]//Proc of ACM SIGCOMM. New York: ACM, 2005.. 229-240
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  • 5Zuev D. Moore A W. Traffic classification using a statistical approach [G]//Dovrolis C. LNCS 3431: Proc of the PAM. Heidelberg, Germany: Springer, 2005:321-324
  • 6Moore A W, Zuev D. Internet traffic classification using Bayesian analysis techniques [C] //Proc of the 2005 ACM SIGMETRICS Int Conf on Measurement and Modeling of Computer Systems. New York: ACM, 2005: 50-60
  • 7Tan P N, Steinbach M, Kumar V. Introduction to Data Mining [M]. Boston: Addison Wesley, 2006
  • 8Moore A W, Zuev D, Crogan M. Discriminators for use in flow-based classification, RR-05-13 [R]. London: Queen Mary University of London, 2005
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  • 10Chang C C, Lin C J. LIBSVM: A library for support vector machines[EB/OL]. 2001 [2007-08-06]. http://www.csie. ntu. edu. tw/-ejlin/libsvm

共引文献211

同被引文献45

引证文献7

二级引证文献14

济南大学学报（自然科学版）
2012年 第3期

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