网络流量识别的自适应分级滑动窗决策树算法  

Traffic identification algorithm based on Hoeffding decision tree with adaptive grading slide windows

作　　者：张剑[1] 曹萍[2] 寿国础[3] 

机构地区：[1]上海工程技术大学航空学院,上海201620 [2]上海对外经贸大学商务信息学院,上海201620 [3]北京邮电大学信息与通信工程学院,北京100876

出　　处：《计算机应用研究》2013年第8期2470-2472,共3页Application Research of Computers

基　　金：国家"863"计划资助项目(2008AA01Z218)

摘　　要：针对网络流量存在概念漂移、不同应用类型数据流偏态分布等特性,提出了基于Hoeffding决策树的自适应分级滑动窗决策树的网络流量识别算法。该算法根据节点信息增益率检测概念漂移、动态调整概念漂移检测窗口及不同类型训练样本集窗口,实现对不同速率概念漂移的自适应分类和决策树更新。实验结果显示新算法对劣势频繁漂移的应用类型的识别准确率与batch C4.5算法接近,比CVFDT算法提高约20%,可以获得更加均衡的不同应用类型分类准确度。Network traffic has characteristics of concept drift, unbalance distribution among different application types. This paper proposed a traffic identification algorithm, named adaptive grading shale window decision tree （ AGSW-DT）, based on Hoeffding decision tree. It realized adaptive detection of concept drift and decision tree update according to the information gain ratio of nodes, and then adjusted concept-drifting detection window and training set windows dynamically in accordance with the detection results. Comparing to the experiment results of batch C4.5 and CVFDT, AGSW-DT algorithm gained approximate precision with batch C4.5 algorithm and higher than that of CVFDT algorithm with 20% in terms of minor frequent concept- drifting application types. The proposed algorithm can obtain more balanced classification accuracy among different application types.

关 键 词：流量识别 数据流 概念漂移 分级滑动窗 

分 类 号：TP393.4[自动化与计算机技术—计算机应用技术]