[1]曹彦珍,何云斌,朱素霞,等.基于 RIPPER 的网络流量分类方法[J].哈尔滨理工大学学报,2017,(05):85-90.[doi:10. 15938/j. jhust. 2017. 05. 016]
 CAO Yan-zhen,HE Yun-bin,ZHU Su-xia,et al.Network Flow Classification Methodrule-Based[J].哈尔滨理工大学学报,2017,(05):85-90.[doi:10. 15938/j. jhust. 2017. 05. 016]
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基于 RIPPER 的网络流量分类方法()
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《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2017年05期
页码:
85-90
栏目:
材料科学与工程
出版日期:
2017-12-30

文章信息/Info

Title:
Network Flow Classification Methodrule-Based
文章编号:
1007-2683(2017)05-0085-06
作者:
曹彦珍 何云斌 朱素霞 孙广路
(哈尔滨理工大学 计算机科学与技术学院,黑龙江 哈尔滨 150080)
Author(s):
CAO Yan-zhen HE Yun-bin ZHU Su-xia SUN Guang-lu
School of Computer Science and Technology,Harbin University of Science and Technology,Harbin,150080,China
关键词:
网络流量分类规则学习重复增量式降低错误剪枝不平衡数据
Keywords:
traffic classification rule-based learning repeated incremental pruning to produce error eduction unbalanced data
分类号:
TP393
DOI:
10. 15938/j. jhust. 2017. 05. 016
文献标志码:
A
摘要:
利用一种规则学习方法中的重复增量式降低错误剪枝方法解决网络流量分类问题。利用该方法能够挖掘出网络流属性特征和类别之间的相关关系,并将挖掘出的关系构成分类器用于网络流量分类。该方法能够解决传统机器学习方法在网络流量中有大量的不平衡数据集时,分类错误率高等问题。实验证明,该方法在网络流量分类标准数据集上具有很高的分类准确率、查全率和查准率。
Abstract:
In this paper,repeated incremental pruning to produce error reduction which is a rule learning ethod is used to solve network traffic classification. The method can be used to dig out the correlations between ttributes and classes,which are utilized to build a classifier for traffic classification. The proposed method can ecrease the classification error rate when the traditional machine learning method has a large number of imbalanced ata sets in the network traffic. Experiments show that the method has a very high classification of accuracy,recall and recision in network traffic classification standard data sets.
更新日期/Last Update: 2017-11-28