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Impact of Asymmetry of Internet Traffic for Heuristic Based Classification
Chris Richter, Michael Finsterbusch, Klaus HanBgen, Jean-Alexander Muller
Pages - 167 - 176     |    Revised - 15-11-2012     |    Published - 31-12-2012
Volume - 4   Issue - 5    |    Publication Date - December 2012  Table of Contents
Flow Classification, Internet Traffic, Traffic Identification
Accurate traffic classification is necessary for many administrative networking tasks like security monitoring, providing Quality of Service and network design or planning. In this paper we illustrate the accuracy of 18 different machine learning algorithms with different statistical parameter combinations. Additionally, we divide the statistical parameters into upstream and downstream to observe the influence of the protocol inherent differences of client and server behaviour for traffic classification. Our results show that this differentiation can increase the protocol detection rate and decrement the processing time.
CITED BY (1)  
1 Finsterbusch, M., & Muller, J. A. (2013, October). Formal methods to improve the identification and validation of network traffic. In Network Protocols (ICNP), 2013 21st IEEE International Conference on (pp. 1-3). IEEE.
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2 Google Scholar
3 CiteSeerX
4 refSeek
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1 “Network Based Application Recognition (NBAR),” Cisco® , Oct. 2011,http://www.cisco.com/en/US/products/ps6616/ products ios protocol group home.html.
2 “Application Layer Packet Classifier for Linux,” Oct. 2011, http://l7-filter.sourceforge.net.
3 L. Bernaille et al., “Traffic classification on the fly,” SIGCOMM Comput. Commun. Rev.,vol. 36, April 2006. [Online]. Available: http://doi.acm.org/10.1145/1129582.1129589
4 H. Jiang et al., “Lightweight application classification for network management,” in Proceedings of the 2007 SIGCOMM workshop on Internet network management, ser.INM ’07. New York, NY, USA: ACM, 2007, pp. 299–304. [Online]. Available:http://doi.acm.org/10.1145/1321753.1321771
5 T. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” IEEE In Communications Surveys & Tutorials, vol. 10, no. 4, pp.56–76, 2008.
6 S. Zander, T. Nguyen, and G. Armitage, “Automated traffic classification and application identification using machine learning,” in Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on, Nov. 2005, pp. 250–257.
7 P. Piskac and J. Novotny, “Using of time characteristics in data flow for traffic classification,” ser. AIMS’11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 173–176.[Online]. Available: http://dl.acm.org/citation.cfm?id=2022216.2022243
8 A. W. Moore and D. Zuev, “Internet traffic classification using bayesian analysis techniques,” in Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems, ser. SIGMETRICS ’05. New York, NY,USA: ACM, 2005, pp. 50–60. [Online]. Available:http://doi.acm.org/10.1145/1064212.1064220
9 “TCPDUMP & LibPCAP,” http://www.tcpdump.org.
10 M. Finsterbusch, C. Richter, and J.-A. M¨uller, “Parameter Estimation for Heuristic Based Internet Traffic Classification,” in ICIMP 2012: The Seventh International Conference on Internet Monitoring and Protection, IARIA, Ed. Stuttgart, Germany: IARIA, 2012, ISBN:978-1-61208-201-1.
11 M. Canini, W. Li, and A. W. Moore, “GTVS: boosting the collection of application traffic ground truth,” University of Cambridge, Tech. Rep. UCAM-CL-TR-748, 2009. [Online].Available: http://www.cl.cam.ac.uk/techreports/UCAM-CL-TR-748.pdf
12 M. Hall et al., “The WEKA Data Mining Software: An Update,” SIGKDD Explorations, vol.11, no. 1, 2009.
13 K. O’Hair, “HPROF: A Heap/CPU Profiling Tool in J2SE 5.0.”
14 A. W. Moore, M. L. Crogan, and D. Zuev, “Discriminators for use in flow-based classification,” Queen Mary University of London, Tech. Rep., 2005.
15 A. Finamore et al., “Kiss: Stochastic packet inspection classifier for udp traffic,”Networking, IEEE/ACM Transactions on, vol. 18, no. 5, pp. 1505 –1515, 2010.
16 C. Rotsos et al., “Probabilistic graphical models for semi-supervised traffic classification,”in Proceedings of the 6th International Wireless Communications and Mobile Computing Conference, ser. IWCMC ’10. New York, NY, USA: ACM, 2010, pp. 752–757. [Online].Available: http://doi.acm.org/10.1145/1815396.1815569.
Mr. Chris Richter
HTWK Leipzig - Germany
Mr. Michael Finsterbusch
HTWK Leipzig - Germany
Mr. Klaus HanBgen
- Germany
Mr. Jean-Alexander Muller
- Germany