摘要
针对点对点(P2P)用户习惯、运行环境的异构性,提出P2P流量识别的双层模型。该模型由单流内部流量特征的贝叶斯网络识别算法与多流之间行为特征的支持向量机识别算法组成。实验结果表明,相对于统计特征识别方法,该模型检测准确度提高5.4%,且对于不同应用场景具有较好的稳定性。
Considering the heterogeneity of Peer-to-Peer(P2P) users habit and runtime environment, this paper proposes a two-layered model of P2P traffic identification to identify and filter the P2P traffic. It combines both an identification algorithm of Bayesian network based on single traffic feature method and an identification algorithm of Support Vector Machine(SVM) based on mutti-traffic behavior method. Experimental results show that the method is 5.4% more accurate than the statistical feature identify method, and it has better stability in different application scenes.
出处
《计算机工程》
CAS
CSCD
2012年第16期182-184,188,共4页
Computer Engineering
基金
国家"863"计划基金资助项目(2009AA01Z431)
国家自然科学基金资助项目(61103015)
湖南省自然科学基金资助项目(09JJ5043)
关键词
流量识别
点对点
双层模型
贝叶斯网络
支持向量机
行为特征
traffic identification
Peer-to-Peer(P2P)
two-layered model
Bayesian network
Support Vector Machine(SVM)
behavior feature