计算机工程2011，Vol.37Issue(12)：101-103,3.DOI:10.3969/j.issn.1000-3428.2011.12.034
基于CVFDT的网络流量分类方法
Network Traffic Classification Method Based on Concept-adapting Very Fast Decision Tree
朱欣 1赵雷 1杨季文1
作者信息
- 1. 苏州大学计算机科学与技术学院,江苏苏州215006
- 折叠
摘要
Abstract
Considering Internet data stream dynamically in large volumes, this paper proposes a traffic classification method using data stream mining techniques, named Concept-adapting Very Fast Decision Tree(CVFDT). CVFDT is capable of processing dynamic datasets, coping with concept drift and updating the model catering to incoming data. The approach and naive Bayes method on network traffic data stream sets are tested,which has 12 significant attributes. Experimental result shows that the approach gets high performance on classification accuracy and spatial stability compared with naive Bayes method.关键词
流量分类/应用识别/概念自适应快速决策树/数据流挖掘Key words
traffic classification/ application identification/ Concept-adapting Very Fast Decision Tree(CVFDT)/ data stream mining分类
信息技术与安全科学引用本文复制引用
朱欣,赵雷,杨季文..基于CVFDT的网络流量分类方法[J].计算机工程,2011,37(12):101-103,3.
