[1]李晟锴.基于决策树的P2P流量识别方法研究[J].计算机技术与发展,2011,(12):29-32.
　LI Sheng-kai.P2P Network Traffic Classification Based on Decision Tree[J].,2011,(12):29-32.
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基于决策树的P2P流量识别方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2011年12期
页码:
29-32
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
P2P Network Traffic Classification Based on Decision Tree
文章编号:
1673-629X（2011）12-0029-04
作者:
李晟锴
安徽理工大学计算机科学与工程学院
Author(s):
LI Sheng-kai
School of Computer Science ＆ Engineering,Anhui University of Science ＆ Technology
关键词:
决策树; 流量识别; 特征选择; 分类精度
Keywords:
decision tree ; traffic classification;  feature selection;  classification accuracy
分类号:
TP391
文献标志码:
A
摘要:
针对新型P2P业务采用净荷加密和伪装端口等方法来逃避检测的问题，提出了一种基于决策树的P2P流量识别方法。该方法将决策树方法应用于网络流量识别领域，以适应网络流量的识别要求。决策树方法通过利用训练数据集中的信息熵来构建分类模型，并通过对分类模型的简单查找来完成未知网络流样本的分类。实验结果验证了C4．5决策树算法相比较NaYveBayes、BayesNetwork算法，处理相对简单且计算量不大，具有较高的数据处理效率和分类精度，能够提高网络流量分类精度，更适用于P2P流量识别
Abstract:
To solve the question of new P2P application with payload encryption and camouflage to evade detection port,propose P2P net- work traffic classification based on decision tree. This method applies decision tree into the areas of network traffic to accommodate Internet traffic identification requirements. Decision tree method builds a classification model using information entropy in training data and classifies flows just by a simple march of the decision tree. Compared with Naive Bayes, Bayes network algorithm, experimental results demonstrate the C4.5 decision tree can achieve high classification accuracy with faster computational time by relatively simple and small calculation processing. It is more suitable to P2P traffic identification

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备注/Memo

备注/Memo:
安徽省高等学校自然科学基金重点项目（KJ2009A093）李晟锴（1985-），男，硕士研究生，研究方向为计算机网络、人工智能
更新日期/Last Update: 1900-01-01