基于RIPPER的网络流量分类方法

Network Flow Classification Methodrule-Based

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。

In this paper,repeated incremental pruning to produce error reduction which is a rule learning method is used to solve network traffic classification. The method can be used to dig out the correlations between attributes and classes,which are utilized to build a classifier for traffic classification. The proposed method can decrease the classification error rate when the traditional machine learning method has a large number of imbalanced data sets in the network traffic. Experiments show that the method has a very high classification of accuracy,recall and precision in network traffic classification standard data sets.