Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
권호 표지
DOI QR코드

DOI QR Code

Traffic Classification based on Adjustable Convex-hull Support Vector Machines

조절할 수 있는 볼록한 덮개 서포트 벡터 머신에 기반을 둔 트래픽 분류 방법

  • Yu, Zhibin (School of Electronic and Computer Science, Kyungpook National University) ;
  • Choi, Yong-Do (School of Computer Science and Engineering, Kyungpook National University) ;
  • Kil, Gi-Beom (School of Electronic and Computer Science, Kyungpook National University) ;
  • Kim, Sung-Ho (School of Computer Science and Engineering, Kyungpook National University)
  • 위즈빈 (경북대학교 전자전기컴퓨터학부) ;
  • 최용도 (경북대학교 컴퓨터학부) ;
  • 길기범 (경북대학교 전자전기컴퓨터학부) ;
  • 김승호 (경북대학교 컴퓨터학부)
  • Received : 2011.11.22
  • Accepted : 2011.12.28
  • Published : 2012.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Traffic classification plays an important role in traffic management. To traditional methods, P2P and encryption traffic may become a problem. Support Vector Machine (SVM) is a useful classification tool which is able to overcome the traditional bottleneck. The main disadvantage of SVM algorithms is that it's time-consuming to train large data set because of the quadratic programming (QP) problem. However, the useful support vectors are only a small part of the whole data. If we can discard the useless vectors before training, we are able to save time and keep accuracy. In this article, we discussed the feasibility to remove the useless vectors through a sequential method to accelerate training speed when dealing with large scale data.

트래픽 분류는 트래픽 관리하는데 중요한 역할을 차지하고 있다. 전통적인 방법은 P2P와 암호화 트래픽을 제대로 분류할 수 없는 문제가 있다. 서포트 벡터 머신은 기존의 문제를 해결할 수 있고 병목 현상을 극복할 수 있는 유용한 분류 도구이다. 하지만 서포트 벡터 머신의 주요 장점은 이차 프로그래밍(QP)문제 때문에 큰 데이터 집단을 훈련하는데 시간을 소모한다. 그러나 유용한 서포트 벡터는 전체 데이터에서 극히 일부분이다. 만약 우리가 훈련전에 쓸모없는 벡터들을 삭제할 수 있다면, 시간을 절약하고 정확도를 유지할 수 있다. 이 논문에서 우리는 대규모 데이터를 다룰 때 훈련 속도를 빠르게 하기위해 순차적인 방법을 통해 쓸모없는 벡터들을 제거하기 위한 가능성을 논의하였다.

Keywords

References

  1. Karagiannis, Thomas; Broido, Andre; Faloutsos, Michalis and Claffy, Kc claffy, Transport Layer Identification of P2P Traffic, Internet Measurement Conference (IMC '2004), October 2004.
  2. Madhukar, Alok. and Williamson, Carey., A Longitudinal Study of P2P Traffic Classification, 14th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, September, 2006.
  3. Moore, Andrew W. and Papagiannaki, Konstantina, Toward the Accurate Identification of Network Applications, Passive and Active Measurement Workshop (PAM 2005), March 2005.
  4. Thomas Karagiannis, Andre Broido, Nevil Brownlee, and Kc Claffy, Is P2P dying or just hiding? in Proceedings of the 47th annual IEEE Global Telecommunications Conference (Globecom 2004), Dallas, Texas, USA, November/December 2004.
  5. Subhabrate Sen, Oliver Spatscheck, and Dongmei Wang, Accurate, scalable in network identification of P2P traffic using application signatures, in WWW2004, New York, NY, USA, May 2004.
  6. Jeffrey Erman, Anirban Mahanti, and Martin Arlitt, Byte me: a case for byte accuracy in traffic classification, in MineNet '07: Proceedings of the 3rd annual ACM workshop on Mining network data. New York, USA: ACM Press, June 2007, pp.35-38.
  7. Matthew Roughan, Subhabrata Sen, Oliver Spatscheck, and Nick Duffield, Class-of-service mapping for QoS: A statistical signature-based approach to IP traffic classification, in Proceedings of ACM/SIGCOMM Internet Measurement Conference (IMC) 2004, Taormina, Sicily, Italy, October 2004.
  8. Thuy T. T. Nguyen, Grenville Armitage, "A survey of techniques for internet traffic classification using machine learning," in IEEE Communications Surveys & Tutorials, vol. 10, no. 4, Mar 2008, pp. 56-76. https://doi.org/10.1109/SURV.2008.080406
  9. Este, A.; Gringoli, F.; Salgarelli, L., On-line SVM traffic classification, Wireless Communications and Mobile Computing Conference (IWCMC), 2011 7th International.
  10. Ai-min Yang; Sheng-yi Jiang; He Deng, A P2P Network Traffic Classification Method Using SVM, Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference.
  11. Thorsten Joachims, Making Large-scale Support Vector Machine Learning Pratical, in Advances in Kernel Methods-Support Vector Learning. MIT Press (2000) 169-184.
  12. Nocedal, Jorge; Wright, Stephen J. (2006). Numerical Optimization (2nd ed.). Berlin, New York: Springer-Verlag. p.449. ISBN 978-0-387-30303-1.
  13. John C. Platt, Fast training of support vector machines using sequential minimal optimization, in Adv. in Kernel Methods: Scholkopf, C. Burges, A. Smola (eds.), 1998.
  14. Xuchun Li, Yan Zhu and Eric Sung Sequential Bootstrappe Support Vector Machines A SVM Accelerator in Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, July 31-August 4, 2005.
  15. Zhu Li, Cho, Kenjiro; Fukuda, Kenshue; Esaki, Hiroshi and Kato, Akira, The Impact and Implications of the Growth in Residential User-to-User Traffic, ACM SIGCOMM 2006, Pisa, Italy, September 2006.
  16. Kwang In Kim, Keechul Jung and Jin Hyung Kim, Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1631-1639, Dec 2003. https://doi.org/10.1109/TPAMI.2003.1251157
  17. Kuan-Ming Lin and Chih-Jen Lin, A study on reduced support vector machines, IEEE Transaction on Neural Networks, vol.14, pp.1449-1469, 2003. https://doi.org/10.1109/TNN.2003.820828
  18. Jose Balcazar, Yang Dai and Osamu Watanabe, A random sampling technique for training support vector machines, in Proceedings of the 12th International Conference on Algorithmic Learning Theory, 2001, pp.119-134.
  19. Hyunjung Shin and Sungzoon Cho, Fast pattern selection for support vector classifiers, in Advances in Knowledge Discovery and Data Mining, 7th Pacific-Asia Conference, 2003, pp.376-387.