南京邮电大学学报（自然科学版）2011，Vol.31Issue(4)：101-105,111,6.
基于集成学习的P2P流量识别模型
P2P Traffic Identification Model Based on Ensemble Learning
摘要
Abstract
P2P traffic identification model is constructed based on the ensemble learning algorithm ,which integrate DTNB,ONER and BP neural network algorithm.Using network flow characteristics and the integrated classification algorithm for rule generation in machine learning,network traffic flow is divided into two types,P2P and non-P2P traffic.The model consists of three steps,I.E.Gaining network flow characteristics,P2P traffic feature selection and the establishment of flow classification model.The rationality of the model and the effectiveness of the proposed method are evaluated using method of combining T-fold cross-validation and test sets.The experiment results have shown that the average precision of traffic classification reaches 97.27% and the model has relatively high P2P flow identification accuracy.关键词
P2P流量识别/集成学习/DTNB/OneR/BPKey words
P2P traffic identification/ensemble learning/DTNB/OneR/BP分类
信息技术与安全科学引用本文复制引用
赵丹,王汝传,徐鹤..基于集成学习的P2P流量识别模型[J].南京邮电大学学报（自然科学版）,2011,31(4):101-105,111,6.基金项目
国家自然科学基金(60973139、60773041)、国家和江苏省博士后基金(20100471353、20100471355、20100471356)、江苏高校科技创新计划项目(CX09B_153Z,CX10B-260Z,CX10B-261Z,CX10B-262Z,CX10B-263Z)、江苏省六大高峰人才项目(2008118)、省级现代服务业发展专项资金、江苏省计算机信息处理技术重点实验室基金(2010)资助项目 （60973139、60773041）
