A Network Traffic Classification Method for Class-Imbalanced Data

Xiaohui Guan; Yaguan Qian

doi:10.11959/j.issn.1000-0801.2015085

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A Network Traffic Classification Method for Class-Imbalanced Data

research and development | 更新时间：2024-06-05

- A Network Traffic Classification Method for Class-Imbalanced Data
- Telecommunication Science Vol. 31, Issue 6, Pages: 79-85(2015)
- 作者机构：
  
  1. 浙江水利水电学院杭州 310018
  2. 浙江科技学院理学院杭州 310023
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2015085
  CLC：
- Published Online：2015-06，
  
  Published：20 June 2015
- 稿件说明：
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Xiaohui Guan, Yaguan Qian. A Network Traffic Classification Method for Class-Imbalanced Data[J]. Telecommunication science, 2015, 31(6): 79-85.
DOI：

Xiaohui Guan, Yaguan Qian. A Network Traffic Classification Method for Class-Imbalanced Data[J]. Telecommunication science, 2015, 31(6): 79-85. DOI： 10.11959/j.issn.1000-0801.2015085.

摘要

在网络入侵流量检测中，普遍存在不同攻击类型的流量分布不均现象，导致少数攻击流量类识别率较低。为解决此类问题，基于不同特征空间的分类器流水线组合方法将多分类问题转化为不同特征空间上的两分类问题，有效地实现少数类重抽样和特征空间的优化，避免了少数类受多数类特征的干扰。实验表明，此方法可以有效地提高攻击流量中少数类的分类精度和召回率。

Abstract

It is very common that flow distribution of class is not uniform in attack traffic. It wi11 lead to a 1ow classification accuracy in network intrusion detection. For overcoming this class imbalance phenomenon，a pipelining ensemble approach in different feature spaces was proposed，which translates multi-class classification to two-class classification. Based on the pipelining ensemble，it could be further conduct oversampling and customized feature selection for minority class，which may avoid the disturbance from majority class. The experiment result shows that the proposed approach can efficiently improve the accuracy of minority class of attack traffic.

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references

Hettich S ， Bay S D . KDD cup 1999 . http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html ， 2007

Weiss G ， Provost F . Learning when training data are costly：the effect of class distribution on tree induction . Artificial Intelligence Research ， 2003 （ 19 ）： 315 ～ 354

Kubat M ， Matwin S . Addressing the curse of imbalanced data sets：one sided sampling . Proceedings of the 14th International Conference on Machine Learning ， Nashville，USA ， 1997

Chen X ， Gerlach B ， Casasent D . Pruning support vectors for imbalanced data classification . Proceedings of IEEE International Joint Conference on Neural Networks ， Montreal，Quebec，Canada ， 2005

Kubat M ， Matwin S . Learning when negative examples abound . Proceedings of the 9th European Conference on Machine Learning ， Prague，Czech Republic ， 1997

Chawla N ， Bowyer K ， Hall L ， et al . SMOTE：synthetic minority over-sampling technique . Artificial Intelligence Research ， 2002 （ 16 ）： 321 ～ 357

Estabrooks A ， Jo T ， Japkowicz N . A multiple resampling method for learning from imbalanced data sets . Computational Intelligence ， 2004 ， 20 （ 1 ）： 18 ～ 36

Ling C ， Li C . Data mining for direct marketing problems and solutions . Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining ， New York，USA ， 1998

Chawla N ， Japkowicz N ， Kotcz A . Editorial：special issue on learning from imbalanced data sets . ACM SIGKDD Explorations Newsletter ， 2004 ， 6 （ 1 ）： 1 ～ 6

Putten P V D ， Someren M V . A bias-variance analysis of a real world learning problem：the CoIL challenge 2000 . Machine Learning ， 2004 ， 57 （ 1-2 ）： 177 ～ 195

Forman G . An extensive empirical study of feature selection metrics for text classification . Machine Learning Research ， 2003 （ 3 ）： 1289 ～ 1305

Zheng Z ， Wu X ， Srihari R . Feature selection for text categorization on imbalanced data . ACM SIGKDD Explorations Newsletter ， 2004 （ 6 ）： 80 ～ 89

Zhang H ， Lu G ， Qassrawi M T ， et al . Feature selection for optimizing traffic classification . Computer Communications ， 2012 ， 35 （ 12 ）： 1457 ～ 1471

Galar M ， Fernández A ， Barrenechea E ， et al . A review on ensembles for the class imbalance problem：bagging，boosting，and hybrid-based approaches . IEEE Transactions on Systems，Man，and Cybernetics，Part C：Applications and Reviews ， 2012 ， 42 （ 4 ）： 463 ～ 484

Seiffert C ， Khoshgoftaar T ， Van Hulse J ， et al . Rusboost：ahybrid approach to alleviating class imbalance . IEEE Transactions on Systems，Man and Cybernetics，Part A：Systems and Humans ， 2010 ， 40 （ 1 ）： 185 ～ 197

Liu X Y ， Wu J ， Zhou Z H . Exploratory undersampling for class-imbalance learning . IEEE Transactions on Systems，Man，and Cybernetics，Part B：Cybernetics ， 2009 ， 39 （ 2 ）： 539 ～ 550

Sun Y ， Kamel M S ， WongA K ， et al . Cost-sensitive boosting for classification of imbalanced data . Pattern Recognition ， 2007 ， 40 （ 12 ）： 3358 ～ 3378

Van Hulse J ， Khoshgoftaar T ， Napolitano A . An empirical comparison of repetitive undersampling techniques . Proceedings of IEEE International Conference on Information Reuse ＆Integration ， Las Vegas，NN，USA ， 2009 ： 29 ～ 34

Breiman L . Bagging predictors . Machine Learning ， 1996 （ 24 ）： 123 ～ 140

Xie G ， Iliofotou M ， Keralapura R ， et al . Subflow：towards practical flow-level traffic classification . Proceedings of IEEE INFOCOM ， Orlando，FL，USA ， 2012

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