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A Novel P2P Traffic Identification Algorithm Based on BPSO and Weighted KNN 被引量：6

一种新的基于BPSO和KNN的P2P流量识别算法(英文)
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摘要 Peer-to-Peer technology is one of the most popular techniques nowadays,and it brings some security issues,so the recognition and management of P2P applications on the internet is becoming much more important. The selection of protocol features is significant to the problem of P2P traffic identification. To overcome the shortcomings of current methods,a new P2P traffic identification algorithm is proposed in this paper. First of all,a detailed statistics of traffic flows on internet is calculated. Secondly,the best feature subset is chosen by binary particle swarm optimization. Finally,every feature in the subset is given a proper weight. In this paper,TCP flows and UDP flows each have a respective feature space,for this is advantageous to traffic identification. The experimental results show that this algorithm could choose the best feature subset effectively,and the identification accuracy is improved by the method of feature weighting. Peer-to-Peer technology is one of the most popular techniques nowadays,and it brings some security issues,so the recognition and management of P2P applications on the internet is becoming much more important. The selection of protocol features is significant to the problem of P2P traffic identification. To overcome the shortcomings of current methods,a new P2P traffic identification algorithm is proposed in this paper. First of all,a detailed statistics of traffic flows on internet is calculated. Secondly,the best feature subset is chosen by binary particle swarm optimization. Finally,every feature in the subset is given a proper weight. In this paper,TCP flows and UDP flows each have a respective feature space,for this is advantageous to traffic identification. The experimental results show that this algorithm could choose the best feature subset effectively,and the identification accuracy is improved by the method of feature weighting.
作者 杜敏 陈兴蜀 谭骏
机构地区 College of Computer Science
出处 《China Communications》 SCIE CSCD 2011年第2期52-58,共7页 中国通信（英文版）
基金 supported in part by National Basic Research Program of China ("973 program") under contract No. 2007CB311106 supported by Special Plan Program of National Information Security ("242 program") under contract No. (242) 2009A82
关键词 traffic identification BPSO feature selection feature weighting traffic identification BPSO feature selection feature weighting
分类号 TP393.02 [自动化与计算机技术—计算机应用技术]
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参考文献15

  • 1KIM J T,PARK H K,PAIK E H.Security Issues in Peer- to-peer Systems. Proceedings of the 7th International Conference on Advanced Communications Technology . 2005
  • 2GERBER A,,HOULE J,NGUYEN H, et al.P2P, the Gorilla in the Cable. Proceedings of National Cable & Telecommunications Association Conference . 2003
  • 3ZANDER S,NGUYEN T,ARMITAGE G.Automated Traffic Classification and Application Identification Using Machine Learning. Proceedings of the IEEE Conference on Local Computer Networks 30th Anniversary . 2005
  • 4LIU Hui,FENG Wenfeng,HUANG Yongfeng, et al.A Peer-to-peer Traffi c Identifi cation Method using Machine Learning. Proceedings of International Conference on Networking, Architecture, and Storage (NAS 2007) . 2007
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同被引文献188

  • 1孟姣,王丽宏,熊刚,姚垚.基于机器学习的SSH应用分类研究[J].计算机研究与发展,2012,49(S2):153-159. 被引量：2
  • 2SOYSAL M, CHMfDT E G. Machine Learning Algorithms for Accurate Flow-Based Network Traffic Classification: Evaluation and Com- parison[J]. Performance Evaluation, 2010, 67(6): 452-467.
  • 3SEN S, WANG Jia. Analyzing Peer-to-Peer Traffic Across Large Networks[J]. IEEE/ACM Transactions on Networking, 2004, 12(2): 219-232.
  • 4MOORE A, PAPAGIANNAKI K. Toward the Ac- curate Identification of Network Applica- tions[J]. Lecture Notes in Computer Science, 2005. 3431: 41-54.
  • 5GERBER A, HOULE J, NGUYEN H, etaL P2P, the Gorilla in the Cable[C]// Proceedings of the 2003 National Cable & Telecommunications Association Conference: June 8-11. 2003.Chicago, FL, USA.
  • 6MUELLER M L, ASGHARI H. Deep Packet In- spection and Bandwidth Management: Battles over BitTorrent in Canada and the United States[J]. Telecommunications Policy, 2012, 36(6): 462-475.
  • 7KERALAPURA R, NUCCI A, CHUAH C N. A Novel Self-Learning Architecture for P2PTraffic Classification in High Speed Net- work[J]. Computer Networks, 2010, 54(7): 1055-1068.
  • 8KARAGIANNIS T, BROIDO A, FALOUTSOS M, et al. Transport Layer Identification of P2P Traffic[C]// Proceedings of the 4th ACM SIGCOMM Conference on Internet Measure- ment: October 25-27, 2004. Taormina, Sicily, Italy, 2004: 121-134.
  • 9XU Ke, ZHANG Ming, YE Mingjiang, et al. Identify P2P Traffic by Inspecting Data Trans- fer Behavior[J]. Computer Communications, 2010, 33(10): 1141-1150.
  • 10ESTE A, GRINGOLI F, SALGARELLI L. Support Vector Machines for TCP Traffic Classifica- tion[J]. Computer Networks, 2009, 53(14): 2476-2490.

引证文献6

二级引证文献24

China Communications
2011年 第2期

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