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基于DPI和机器学习方法传输层检测的P2P流量识别模型
引用本文:桑寅,孟少卿,鹿凯宁.基于DPI和机器学习方法传输层检测的P2P流量识别模型[J].电子测量技术,2011,34(10):45-48.
作者姓名:桑寅  孟少卿  鹿凯宁
作者单位:天津大学网络与信息中心 天津300072
摘    要:如何快速而准确的检测出P2P流量,是如今网络管理中的1个重要的问题.现在常见的检测方法有基于端口检测法,DPI深度包检测,以及根据传输层特征来检测.DPI深度包检测方法需要及时跟新特征库,对于加密协议无法识别等缺陷限制了其应用.机器学习的传输层检测方法通过分析流的统计特征来检测P2P流量.较之DPI,该方法能检测出DP...

关 键 词:P2P流量  深度包检测  机器学习  传输层检测

A novel method for P2P traffic identification based on DPI and maching learning
Sang Yin,Meng Shaoqing,Lu Kaining.A novel method for P2P traffic identification based on DPI and maching learning[J].Electronic Measurement Technology,2011,34(10):45-48.
Authors:Sang Yin  Meng Shaoqing  Lu Kaining
Affiliation:Sang Yin Meng Shaoqing Lu Kaining(Information and Network Center of Tianjin University,Tianjin University,Tianjin 300072)
Abstract:A fast and accurate method to identify P2P traffic plays an important role in network management.Port-based classification,DPI(deep payload inspection) and flow-based classification are 3 main methods to inspect network traffic.DPI is widely used nowadays.But the approach is limited by the fact that classification rules must update when new P2P applications appear and it is effect less when faces the encrypted flows.Machine learning flow-based technique is based on per-flow statistics.Comparing with DPI,it ...
Keywords:P2P flow  DPI  machine learning  flow-based classification  
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