基于时序流的移动流量实时分类方法  

A Real-Time Mobile Traffic Classification Approach Based on Timing Sequence Flow

作　　者：刘翼[1,2] 嵩天 廖乐健[1] LIU Yi;SONG Tian;LIAO Le-jian(School of Computer Science,Beijing Institute of Technology,Beijing 100081,China;Network Information Center,Yan＇an University,Yan＇an,Shaanxi 716000,China)

机构地区：[1]北京理工大学计算机学院,北京100081 [2]延安大学网络信息中心,陕西延安716000

出　　处：《北京理工大学学报》2018年第5期537-544,共8页Transactions of Beijing Institute of Technology

基　　金：国家自然科学基金资助项目(61672101;61751217;U1636119);陕西省教育厅科研计划项目(14JK1825);延安市科技攻关资助项目(2014KG-09)

摘　　要：移动互联网的快速发展,产生了网络测量、网络安全和服务质量等方面的新问题.为了深入研究移动互联网的特性,研究人员需要从传统网络流量中快速准确分类出移动流量.本文提出了一种采用轻量级流表与深度数据包检测技术(DPI)相结合的移动流量实时分类方法,将网络流按照时间间隔关系扩展为时序流,并通过DPI时序流前N个特征数据包准确地分类出移动流量,缩减了流表规模,减少了实际DPI开销.通过实时的网络流量实验表明,DPI时序流前8个特征数据包时,提出的方法识别准确率达到91.55%,单次深度数据包检测的平均开销为20个数据包,并且流表的规模缩减到原来的0.21%.与P0F比较,方法识别准确率等性能有明显提升.The rapid development of mobile Internet brings many special problems in the fields of network security,network measurement and quality of service.In order to further study the characteristics of mobile Internet,researchers need to quickly and accurately classify the mobile traffic flow from the traditional network traffic.In this paper,combining lightweight flow table and deep packet inspection（DPI）technology,a real-time mobile network traffic classification approach was proposed.To reduce the scale of flow table,DPI overhead and improves the accuracy of mobile traffic classification,the network flow was expanded into the sequence flow segments according to the interval-time relationship,and the mobile traffic was classified accurately according to DPI of first N packets in the sequence flow segments.The real-time network traffic experiments show that,the identification accuracy rate can reach 91.55%,the average overhead of one DPI only takes 20 packets,and the scale of flow table can be reduced to0.21%.Compared with the P0 F,the accuracy of the propose approach can be improved significantly.

关 键 词：流量分类 移动流量 深度数据包检测 实时 HTTP协议 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]