›› 2012, Vol. 42 ›› Issue (04): 947-951.

Previous Articles     Next Articles

P2P traffic identification based on support vector data description

LIU San-min1,2, SUN Zhi-xin1,3,4   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;
    2. College of Computer and Information, Anhui Polytechnic University, Wuhu 241000, China;
    3. College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;
    4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
  • Received:2011-02-24 Online:2012-07-01 Published:2012-07-01

PDF (PC)

315

Abstract: In light of the idea of clustering, the model of P2P traffic identification was constructed by Support Vector Data Description (SVDD). First, Principal Component Analysis (PCA) was introduced to reduce dimensions of the data set. Then, the classified model was established by support vector samples, which come from the minimal sphere by the principle of SVDD. Finally, the class of the test samples was determined by the minimal distance between the center of sphere and the sample. The model is simple and suitable for P2P traffic identification environment. It overcomes the shortcoming of the current research based on machine learning in model complexity and data unbalance. Experiment results show the high accuracy and reliability of the proposed model.

Key words: computer system organization, support vector data description, principal component analysis, P2P traffic identification

CLC Number: 

  • TP393
[1] Zuev D, Moore A W. Traffic classification using a statistical approach//Proc of the PAM, LNCS 3431, Heidelberg:Springer-Verlag, 2005:321-324.
[2] Moore A W, Zuev D. Internet traffic classification using Bayesian analysis techniques//Proc of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, 2005:50-60.
[3] Tom Auld, Moore A W, Stephen F G. Bayesian neural networks for internet traffic classification[J]. IEEE Transactions on Neural Networks, 2007, 18(1):223-239.
[4] Erman J,Mahanti A,Arlitt M.Internet traffic identification using machine learning techniques//Proc of the 49th IEEE GLOBECOM, San Francisco, 2006.
[5] Alice Este, Francesco Gringoli, Luca Salgarelli. Support vector machines for TCP traffic classification[J]. Computer Networks, 2009, 53:2476-2490.
[6] 孙知信, 张玉峰. 基于多维支持向量机的P2P网络流量识别模型[J]. 吉林大学学报:工学版, 2010, 40(5):1298-1302. Sun Zhi-xin,Zhang Yu-feng.P2P network traffic identification model based on MSVM[J]. Journal of Jilin University (Engineering and Technology Edition), 2010, 40(5):1298-1302.
[7] Tax D, Duin R P. Support vector domain description[J]. Pattern Recognition Letters, 1999, 20:1191-1199.
[8] Tax D, Duin R P. Support vector data description[J]. Machine Learning, 2004, 54(1):45-66.
[9] Moore A W, Zuev D, Crogan M. Discriminators for use in flow-based classification, RR-05-13. London:Queen Mary University of London, 2005.
[1] LIU Zhe, XU Tao, SONG Yu-qing, XU Chun-yan. Image fusion technology based on NSCT and robust principal component analysis model with similar information [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(5): 1614-1620.
[2] ZHANG Man, SHI Shu-ming. Analysis of state transition characteristics for typical vehicle driving cycles [J]. 吉林大学学报(工学版), 2018, 48(4): 1008-1015.
[3] GENG Qing-tian, YU Fan-hua, WANG Yu-ting, GAO Qi-kun. New algorithm for vehicle type detection based on feature fusion [J]. 吉林大学学报(工学版), 2018, 48(3): 929-935.
[4] ZHAO Bo, QIN Gui-He, ZHAO Yong-Zhe, YANG Wen-Di. Public key cryptosystem based on semi-trapdoor one-way function [J]. 吉林大学学报(工学版), 2018, 48(1): 259-267.
[5] YU Bin-bin, WU Xin-yu, CHU Jian-feng, HU Liang. Signature protocol for wireless sensor network based on group key agreement [J]. 吉林大学学报(工学版), 2017, 47(3): 924-929.
[6] SU Chang, FU Li-ming, WEI Jun, LI Shuo, HUANG Lei, CAO Yue. Design method in exterior color based Kansei engineering and principal component analysis [J]. 吉林大学学报(工学版), 2016, 46(5): 1414-1419.
[7] WEI Xiao-hui, LI Xiang, LI Hong-liang, LI Cong, ZHUANG Yuan, YU Hong-mei. Flexible Online MapReduce model and topology protocols supporting large-scale stream data processing [J]. 吉林大学学报(工学版), 2016, 46(4): 1222-1231.
[8] CHE Xiang-jiu, LIANG Sen. Improved algorithm of SPIHT based on Max-Heap tree [J]. 吉林大学学报(工学版), 2016, 46(3): 865-869.
[9] MA Shuang, ZHOU Chang-jiu, ZHANG Lian-dong, HONG Wei, TIAN Yan-tao. Twist-lock online recognition based on improved incremental PCA by Kinect [J]. 吉林大学学报(工学版), 2016, 46(3): 890-896.
[10] QIU Chun-ling, TAO Qiang, FAN Run-long, WANG Pei-zhi. Zircon image matching method based on description of SIFT feature by LBP [J]. 吉林大学学报(工学版), 2014, 44(6): 1793-1798.
[11] FU Shuai, MA Jian-feng, LI Hong-tao, WANG Chang-guang. Improved data aggregation algorithm based on clustered wireless sensor network [J]. 吉林大学学报(工学版), 2014, 44(4): 1118-1125.
[12] GU Bo-yu,SUN Jun-xi,LI Hong-zuo,LIU Hong-xi,LIU Guang-wen. Face recognition based on eigen weighted modular two-directional two-dimensional PCA [J]. 吉林大学学报(工学版), 2014, 44(3): 828-833.
[13] SONG Huai-bo, SHI Jian-qiang. Pose estimation of varied human faces based on PCA method [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 43-46.
[14] WANG Zhuo-zheng, JIA Ke-bin. Application of matrix completion and principal component analysis to corrupted image registration [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 78-83.
[15] BAO Lei, XU Qi-zhi. Spectrum-keeping algorithm for fusing based on PCA [J]. 吉林大学学报(工学版), 2013, 43(增刊1): 88-91.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd