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Inferring applications at the network layer using collective traffic statistics

Published: 14 June 2010 Publication History

Abstract

In this paper, we propose a novel technique for inferring the distribution of application classes present in the aggregated traffic flows between endpoints, which exploits both the statistics of the traffic flows, and the spatial distribution of those flows across the network. Our method employs a two-step supervised model, where the bootstrapping step provides initial (inaccurate) inference on the traffic application classes, and the graph-based calibration step adjusts the initial inference through the collective spatial traffic distribution. In evaluations using real traffic flow measurements from a large ISP, we show how our method can accurately classify application types within aggregate traffic between endpoints, even without the knowledge of ports and other traffic features. While the bootstrap estimate classifies the aggregates with 80% accuracy, incorporating spatial distributions through calibration increases the accuracy to 92%, i.e., roughly halving the number of errors.

References

[1]
T. Karagiannis, K. Papagiannaki and M. Faloutsos. BLINC: Multilevel traffic classification in the dark. In Proc. of ACM SIGCOMM, August 2005.
[2]
Andrew W. Moore and Denis Zuev. Internet traffic classification using bayesian analysis techniques. In Proc. of ACM SIGMETRICS'05, Banff, Canada, 2005.
[3]
Y. Jin, E. Sharafuddin, and Z-L. Zhang. Unveiling core network-wide communication patterns through application traffic activity graph decomposition. In Proc. of SIGMETRICS '09, pages 49--60, 2009.
[4]
Y. Jin, N. Duffield, P. Haffner, S. Sen, and Z.-L. Zhang. A modular machine learning system for flow-level traffic classification in large networks. Technical report, 2010. http://www-users.cs.umn.edu/~yjin/papers/sigmetrics.tech.pdf.
[5]
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3), 2008.

Cited By

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  • (2012)A Modular Machine Learning System for Flow-Level Traffic Classification in Large NetworksACM Transactions on Knowledge Discovery from Data10.1145/2133360.21333646:1(1-34)Online publication date: 1-Mar-2012
  • (2011)Packet tagging system for enhanced traffic profiling2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application10.1109/IMSAA.2011.6156362(1-6)Online publication date: Dec-2011
  • (2020)Automatic depression prediction using Internet traffic characteristics on smartphonesSmart Health10.1016/j.smhl.2020.100137(100137)Online publication date: Sep-2020
  • Show More Cited By

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    Published In

    cover image ACM Conferences
    SIGMETRICS '10: Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
    June 2010
    398 pages
    ISBN:9781450300384
    DOI:10.1145/1811039
    • cover image ACM SIGMETRICS Performance Evaluation Review
      ACM SIGMETRICS Performance Evaluation Review  Volume 38, Issue 1
      Performance evaluation review
      June 2010
      382 pages
      ISSN:0163-5999
      DOI:10.1145/1811099
      Issue’s Table of Contents

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 14 June 2010

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    Author Tags

    1. application identification
    2. collective traffic statistics
    3. graph-based calibration
    4. two-step model

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    Overall Acceptance Rate 459 of 2,691 submissions, 17%

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    Cited By

    View all
    • (2012)A Modular Machine Learning System for Flow-Level Traffic Classification in Large NetworksACM Transactions on Knowledge Discovery from Data10.1145/2133360.21333646:1(1-34)Online publication date: 1-Mar-2012
    • (2011)Packet tagging system for enhanced traffic profiling2011 IEEE 5th International Conference on Internet Multimedia Systems Architecture and Application10.1109/IMSAA.2011.6156362(1-6)Online publication date: Dec-2011
    • (2020)Automatic depression prediction using Internet traffic characteristics on smartphonesSmart Health10.1016/j.smhl.2020.100137(100137)Online publication date: Sep-2020
    • (2019)Towards a Profiling View for Unsupervised Traffic Classification by Exploring the Statistic Features and Link PatternsProceedings of the 2019 Workshop on Network Meets AI & ML10.1145/3341216.3342213(50-56)Online publication date: 14-Aug-2019
    • (2015)Network traffic classification techniques and challenges2015 Tenth International Conference on Digital Information Management (ICDIM)10.1109/ICDIM.2015.7381869(43-48)Online publication date: Oct-2015
    • (2013)Reviewing traffic classificationDataTraffic Monitoring and Analysis10.5555/2555672.2555680(123-147)Online publication date: 1-Jan-2013
    • (2013)Reviewing Traffic ClassificationData Traffic Monitoring and Analysis10.1007/978-3-642-36784-7_6(123-147)Online publication date: 2013

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