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Unexpected means of protocol inference

Published:25 October 2006Publication History

ABSTRACT

Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.

References

  1. Ethereal: A network protocol analyzer. http://www.ethereal.com.Google ScholarGoogle Scholar
  2. S. Baset and H. Schulzrinne. An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol. Technical report, Columbia University, New York, NY, 2004.Google ScholarGoogle Scholar
  3. L. Bernaille, R. Teixeira, I. Akodkenou, A. Soule, and K. Salamatian. Traffic classification on the fly. ACM SIGCOMM Computer Communication Review, 36(2):23--26, April 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. K. Claffy, G. Miller, and K. Thompson. The nature of the best: Recent measurements from an Internet backbone. In Proc. of INET '98, jul, 1998.Google ScholarGoogle Scholar
  5. T. M. Cover and J. A. Thomas. Elements of Information Theory. John Wiley & Sons, 1991. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. C. Dewes, A. Wichmann, and A. Feldmann. An Analysis of Internet Chat Systems. In Proc. of the Second Internet Measurement Workshop (IMW), Nov 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. C. Fraleigh, S. Moon, B. Lyles, C. Cotton, M. Khan, D. Moll, R. Rockell, T. Seely, and C. Diot. Packet-level Traffic Measurements from the Sprint IP Backbone. IEEE Network, 17(6):6--16, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. P. Haffner, S. Sen, O. Spatscheck, and D. Wang. ACAS: Automated construction of application signatures. In Proceedings of the 2005 Workshop on Mining Network Data, pages 197--202, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. IANA. TCP and UDP port numbers. http://www.iana.org/assignments/port-numbers.Google ScholarGoogle Scholar
  10. T. Karagiannis, A. Broido, N. Brownlee, K. Claffy, and M. Faloutsos. Is P2P dying or just hiding? In IEEE Globecom 2004 - Global Internet and Next Generation Networks, Dallas/Texas, USA, Nov, 2004. IEEE.Google ScholarGoogle Scholar
  11. T. Karagiannis, A. Broido, M. Faloutsos, and K. Claffy. Transport Layer Identification of P2P Traffic. In Proc. of the Second Internet Measurement Workshop (IMW), Nov 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. T. Karagiannis, D. Papagiannaki, and M. Faloutsos. BLINC: Multilevel traffic classification in the dark. In Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pages 229--240, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. P. Maymounkov and D. Mazières. Kademlia: A peer-to-peer information system based on the xor metric. In Proceedings of the First International Workshop on Peer-to-Peer Systems (IPTPS), 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. A. Moore and D. Papagiannaki. Toward the Accurate Identification of Network Applications. In Proc. of the Passive and Active Measurement Workshop, mar 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. A. W. Moore and D. Zuev. Internet traffic classification using bayesian analysis techniques. In Proceedings of the 2005 Conference on Measurement and Modeling of Computer Systems, pages 50--60, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. T. Oliver, B. Schmidt, and D. Maskell. Hyper customized processors for bio-sequence database scanning on fpgas. In FPGA '05: Proc. of the 2005 ACMSIGDA 13th international symposium on Field-programmable gate arrays, pages 229--237, New York, NY, USA, 2005. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. V. Paxson. Bro: A System for Detecting Network Intruders in Real-Time. Computer Networks (Amsterdam, Netherlands: 1999), 31(23-24):2435--2463, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. D. Plonka. FlowScan: A Network Traffic Flow Reporting and Visualization Tool. In Proc. of USENIX LISA, jul, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Sanfeliu and K. Fu. A Distance Measure Between Attributed Relational Graphs for Pattern Recognition. IEEE Transactions on Systems, Man and Cybernetics, SMC-13(3):353--362, 1981.Google ScholarGoogle Scholar
  20. S. Sen, O. Spatscheck, and D. Want. Accurate, Scalable In-network Identification of P2P Traffic Using Application Signatures. In Proc. of the 13th International World Wide Web Conference, may 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. T. F. Smith and M. S. Waterman. Identification of Common Molecular Subsequences. Journal of Molecular Biology, 147, 1981. http://gel.ym.edu.tw/~chc/AB_papers03/.pdf.Google ScholarGoogle Scholar
  22. G. Voss, A. Schröder, W. Müller-Wittig, and B. Schmidt. Using Graphics Hardware to Accelerate Biological Sequence Analysis. In Proc. of IEEE Tencon, Melbourne, Australia, 2005.Google ScholarGoogle Scholar
  23. S. Zander, T. Nguyen, and G. Armitage. Self-learning IP Traffic Classification based on Statistical Flow Characteristics. In Proc. of the 6th Passive and Active Network Measurement Workshop, March 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      cover image ACM Conferences
      IMC '06: Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
      October 2006
      356 pages
      ISBN:1595935614
      DOI:10.1145/1177080

      Copyright © 2006 ACM

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      Publication History

      • Published: 25 October 2006

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