摘要
针对网络流量数据大、动态变化性高的问题,提出一种基于数据流挖掘技术——概念自适应快速决策树(CVFDT)的网络流量识别方法。CVFDT适合处理流动数据,随数据样本分布的变化更新模型,并能处理概念漂移。在具有12个最优属性特征的网络流数据集上进行实验,结果表明,与朴素贝叶斯方法相比,CVFDT方法具有较好的分类效果和稳定性。
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.
出处
《计算机工程》
CAS
CSCD
北大核心
2011年第12期101-103,共3页
Computer Engineering
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
traffic classification
application identification
Concept-adapting Very Fast Decision Tree（CVFDT）
data stream mining
