Auto Adaptive Identification Algorithm Based on Network Traffic Flow

Authors

  • Shi Dong Shool of Computer Science and Engineering,Southeast University
  • Xingang Zhang School of Computer and Information Technology, Nanyang Normal University Nanyang, 473061, China
  • Dingding Zhou

Keywords:

Traffic identification, Internet Service Provider (ISP), Auto Adaptive algorithm (AA), asymmetry routing

Abstract

Traffic identification is a key task for any Internet Service Provider (ISP) or network administrator. Machine learning method is an important researchmethod on traffic identification, while impact of the asymmetry router on the  traffic identification is considered, so this paper analyzes the impact of asymmetry routing on traffic identification, and proposes an effective method to decrease the impact, and experimental results show the auto adaptive algorithm can improve the traffic identification.

References

T. Karagiannis, K. Papagiannaki, M. Faloutsos (2005); Blinc: multilevel traffic classification in the dark, in: ACM SIGCOMM Computer Communication Review, ACM, 35: 229-240. http://dx.doi.org/10.1145/1080091.1080119

A. Moore, K. Papagiannaki (2005); Toward the accurate identification of network applications, PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement, 41-54.

A. Moore, D. Zuev (2005); Internet traffic classification using bayesian analysis techniques, in: ACM SIGMETRICS Performance Evaluation Review, ACM, 33:50-60. http://dx.doi.org/10.1145/1064212.1064220

L. Bernaille, R. Teixeira, K. Salamatian (2006), Early application identification, in: Proceedings of the 2006 ACM CoNEXT conference, ACM. http://dx.doi.org/10.1145/1368436.1368445

Wolfgang John, Sven Tafvelin (2007); Differences between in- and outbound internet backbone traffic, in: Proceedings of Terena Networking Conference, TERENA, 1-14.

Hotpotatorouting, http://en.wikipedia.org/wiki/Hot-potato_routing.

N. Williams, S. Zander, G. Armitage, Evaluating machine learning algorithms for automated network application identification, Center for Advanced Internet Architectures, CAIA, Technical Report 060410B, DOI:10.1.1.84.7170.

N. Williams, S. Zander, G. Armitage (2006), A preliminary performance comparison of five machine learning algorithms for practical ip traffic flow classification, ACM SIGCOMM Computer Communication Review 36(5):5-16. http://dx.doi.org/10.1145/1163593.1163596

Z. Li, R. Yuan, X. Guan (2007), Accurate classification of the internet traffic based on the svm method, in: Communications, 2007. ICC'07. IEEE International Conference on, IEEE, 1373-1378. http://dx.doi.org/10.1109/ICC.2007.231

P. Teufl, U. Payer, M. Amling, M. Godec, S. Ruff, G. Scheikl, G.Walzl (2008), Infect-network traffic classification, in:Networking, 2008. ICN 2008. Seventh International Conference on, IEEE, 439-444. http://dx.doi.org/10.1109/ICN.2008.42

T. Kiziloren, E. Germen (2007), Network traffic classification with self organizing maps, in: Computer and information sciences, 2007. iscis 2007. 22nd international symposium on, IEEE, 1-5. http://dx.doi.org/10.1109/ISCIS.2007.4456852

Y. Lim, H. Kim, J. Jeong, C. Kim, T. Kwon, Y. Choi (2010), Internet traffic classification demystified: on the sources of the discriminative power, in: Proceedings of the 6th International COnference, ACM. http://dx.doi.org/10.1145/1921168.1921180

H. Kim, K. Claffy, M. Fomenkov, D. Barman, M. Faloutsos, K. Lee (2008); Internet traffic classification demystified: myths, caveats, and the best practices, in:Proceedings of the 2008 ACM CoNEXT conference, ACM. http://dx.doi.org/10.1145/1544012.1544023

J. Erman, M. Arlitt, A. Mahanti (2006), Traffic classification using clustering algorithms, in: Proceedings of the 2006 SIGCOMM workshop on Mining network data, ACM, 281-286. http://dx.doi.org/10.1145/1162678.1162679

V. Carela-Espanol, P. Barlet-Ros, J. Solé-Pareta (2009), Traffic classification with sampled netflow, DOI:10.1.1.390.5780.

T. Nguyen, G. Armitage (2008), A survey of techniques for internet traffic classification using machine learning, Communications Surveys & Tutorials, IEEE, 10(4):56-76. http://dx.doi.org/10.1109/SURV.2008.080406

A. Callado, C. Kamienski, G. Szabó, B. Gero, J. Kelner, S. Fernandes, D. Sadok (2009), A survey on internet traffic identification, Communications Surveys & Tutorials, IEEE, 11(3):37-52. http://dx.doi.org/10.1109/SURV.2009.090304

M. Zhang, W. John, K. Claffy, N. Brownlee (2009), State of the art in traffic classification: A research review, in:PAM '09: 10th International Conference on Passive and Active Measurement, Student Workshop, Seoul, Korea.

A. Dainotti, A. Pescape, K. Claffy (2012), Issues and future directions in traffic classification, Network, IEEE, 26(1):35-40. http://dx.doi.org/10.1109/MNET.2012.6135854

Z. Mao, L. Qiu, J. Wang, Y. Zhang (2005), On as-level path inference, in: ACM SIGMETRICS Performance Evaluation Review, ACM, 33:339-349.

Y. He, M. Faloutsos, S. Krishnamurthy (2004), Quantifying routing asymmetry in the internet at the as level, in: Global Telecommunications Conference, GLOBECOM'04. IEEE, 3: 1474-1479.

W. John (2008), On measurement and analysis of internet backbone traffic, Thesis for the degree of Licentiate of Engineering, a Swedish degree between M.Sc. and Ph.D., Chalmers University of Technology.

J. Levandoski, E. Sommer, M. Strait, et al.(2008), Application layer packet classifier for linux, http://l7-filter.sourceforge.net/.

*** Lbnl/icsi enterprise tracing project, http://www.icir.org/enterprisetracing.

T. Karagiannis, A. Broido, M. Faloutsos, et al. (2004), Transport layer identification of p2p traffic, in: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, 121-134. http://dx.doi.org/10.1145/1028788.1028804

*** The cooperative association for internet data analysis(caida), http://www.caida.org.

T. Nguyen, G. Armitage (2006), Training on multiple sub-flows to optimise the use of machine learning classifiers in real-world ip networks, in: Local Computer Networks, Proceedings 2006 31st IEEE Conference on, IEEE, 369-376. http://dx.doi.org/10.1109/LCN.2006.322122

W. John, M. Dusi, K. Claffy (2010), Estimating routing symmetry on single links by passive flow measurements, in: Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, ACM, , 473-478. http://dx.doi.org/10.1145/1815396.1815506

*** IP Trace Distribution System, http://iptas.edu.cn/src/system.php.

Published

2014-10-10

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.