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基于代价敏感决策树的网络流量分类研究
引用本文:吴耿,李杰,杨文保.基于代价敏感决策树的网络流量分类研究[J].电脑与信息技术,2011,19(5):5-8.
作者姓名:吴耿  李杰  杨文保
作者单位:中南大学信息科学与工程学院,湖南长沙,410075
摘    要:传统的基于端口的流量分类方法和基于DPI技术的流量分类方法由于P2P技术和加密技术的流行而开始失效。基于网络流特征及机器学习的流量分类方法因为克服了上述弊端而成为了流量分类领域的研究热点。实际网络环境中,“大象流”和“老鼠流”在数量和传输字节量等方面存在着严重的不平衡,降低了基于机器学习流量分类方法的实际分类效果。针对...

关 键 词:网络流量分类  不平衡流量  代价敏感  决策树

Research on Network Traffic Classification Based on Cost-sensitive Decision Tree
WU Geng,LI Jie,YANG Wen-bao.Research on Network Traffic Classification Based on Cost-sensitive Decision Tree[J].Computer and Information Technology,2011,19(5):5-8.
Authors:WU Geng  LI Jie  YANG Wen-bao
Affiliation:WU Geng,LI Jie,YANG Wen-bao(School of Information Science and Engineering,Central South University,Changsha 410075,China)
Abstract:Traditional traffic classification method based on port number and DPI technologies are becoming ineffective as that P2P and encrypt technology becoming popular. Because traffic classification method based on network flow statistics and machine learning can resolve prior defect, it becomes a hot in traffic classification field. In practical network environment, there are serious imbalance in quantity and bytes carried between "elephant flows" and "mouse flows", which impacts the actual classification result of machine learning traffic classification method. To resolve this problem, this paper uses the cost-sensitive decision tree to the traffic classification. Experimental results show that our method has greater "bytes classification accuracy" and suitable to the classification of imbalance network traffic.
Keywords:network traffic classification  unbalanced traffic  cost-sensitive  decision tree
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