skip to main content
10.1145/1269880.1269890acmconferencesArticle/Chapter ViewAbstractPublication PagescommConference Proceedingsconference-collections
Article
Free access

Byte me: a case for byte accuracy in traffic classification

Published: 12 June 2007 Publication History

Abstract

Numerous network traffic classification approaches have recently been proposed. In general, these approaches have focused on correctly identifying a high percentage of total flows. However, on the Internet a small number of "elephant" flows contribute a significant amount of the traffic volume. In addition, some application types like Peer-to-Peer (P2P) and FTP contribute more elephant flows than other applications types like Chat. In this opinion piece, we discuss how evaluating a classifier on flow accuracy alone can bias the classification results. By not giving special attention to these traffic classes and their elephant flows in the evaluation of traffic classification approaches we might obtain significantly different performance when these approaches are deployed in operational networks for typical traffic classification tasks such as traffic shaping. We argue that byte accuracy must also be used when evaluating the accuracy of traffic classification algorithms.

References

[1]
L. Bernaille, R. Teixeira, and K. Salamatian. Early Application Identification. In CoNEXT'06, Lisboa, Portugal, December 2006.
[2]
M. Crotti, M. Dusi, F. Gringoli, and L. Salgarelli. Traffic Classification through Simple Statistical Fingerprinting. Computer Communications Review, 37(1):7--16, 2007.
[3]
J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson. A Semi-Supervised Approach to Network Traffic Classification. In SIGMETRICS'07 (Extended Abstract), San Diego, USA, June 2007.
[4]
J. Erman, A. Mahanti, M. Arlitt, I. Cohen, and C. Williamson. Offline/Online Traffic Classification Using Semi-Supervised Learning. Technical report, University of Calgary, 2007.
[5]
J. Erman, A. Mahanti, M. Arlitt, and C. Williamson. Identifying and Discriminating Between Web and Peer-to-Peer traffic in the Network Core. In WWW'07, Banff, Canada, May 2007.
[6]
P. Haffner, S. Sen, O. Spatscheck, and D. Wang. ACAS: Automated Construction of Application Signatures. In SIGCOMM'05 MineNet Workshop, Philadelphia, USA, August 2005.
[7]
IANA. Internet Assigned Numbers Authority (IANA). http://www.iana.org/assignments/port-numbers.
[8]
T. Karagiannis, A. Broido, M. Faloutsos, and k. claffy. Transport Layer Identification of P2P Traffic. In IMC'04, Taormina, Italy, October 2004.
[9]
T. Karagiannis, K. Papagiannaki, and M. Faloutsos. BLINC: Multilevel Traffic Classification in the Dark. In SIGCOMM'05, Philadelphia, USA, August 2005.
[10]
S. Kumar, S. Dharmapurikar, F. Yu, P. Crowley, and J. Turner. Algorithms to Accelerate Multiple Regular Expressions Matching for Deep Packet Inspection. In SIGCOMM '06, Pisa, Italy, September 2006.
[11]
K. Lan and J. Heidemann. A Measurement Study of Correlations of Internet Flow Characteristics. Computer Networks, 50(1):46--62, 2006.
[12]
J. Ma, K. Levchenko, C. Krebich, S. Savage, and G. Voelker. Unexpected Means of Protocol Inference. In IMC'06, Rio de Janeiro, Brasil, October 2006.
[13]
J. C. Mogul and M. Arlitt. SC2D: An Alternative to Trace Anonymization. In SIGCOMM'06 MineNet Workshop, Pisa, Italy, September 2006.
[14]
A. Moore and K. Papagiannaki. Toward the Accurate Identification of Network Applications. In PAM'05, Boston, USA, March 2005.
[15]
A. Moore and D. Zuev. Internet Traffic Classification Using Bayesian Analysis Techniques. In SIGMETRIC'05, Banff, Canada, June 2005.
[16]
M. Roughan, S. Sen, O. Spatscheck, and N. Duffield. Class-of-Service Mapping for QoS: A Statistical Signature-based Approach to IP Traffic Classification. In IMC'04, Taormina, Italy, October 2004.
[17]
S. Sen, O. Spatscheck, and D. Wang. Accurate, Scalable In-Network Identification of P2P Traffic Using Application Signatures. In WWW'04, New York, USA, May 2004.
[18]
P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2005.
[19]
N. Williams, S. Zander, and G. Armitrage. A Preliminary Performance Comparison of Five Machine Learning Algorithms for Practical IP Traffic Flow Classification. Computer Communication Review, 30:5--16, October 2006.
[20]
K. Xu, Z. Zhang, and S. Bhattacharyya. Profiling Internet Backbone Traffic: Behavior Models and Applications. In SIGCOMM '05, Philadelphia, USA, August 2005.

Cited By

View all
  • (2023)FASTeller: A Hardware Partial Aggregator for Accurate Flow Counting in Cloud Networks2023 IEEE 31st International Conference on Network Protocols (ICNP)10.1109/ICNP59255.2023.10355603(1-12)Online publication date: 10-Oct-2023
  • (2023)Empirical Evaluation of DASH Streaming over Multipath TCP in a Mixed Traffic Environment2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS59832.2023.10468727(680-685)Online publication date: 17-Dec-2023
  • (2023)Impact of class imbalance in VeReMi dataset for misbehavior detection in autonomous vehiclesSoft Computing10.1007/s00500-023-08003-4Online publication date: 22-Mar-2023
  • Show More Cited By

Index Terms

  1. Byte me: a case for byte accuracy in traffic classification

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    MineNet '07: Proceedings of the 3rd annual ACM workshop on Mining network data
    June 2007
    58 pages
    ISBN:9781595937926
    DOI:10.1145/1269880
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 12 June 2007

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. machine learning
    2. traffic classification

    Qualifiers

    • Article

    Conference

    SIGMETRICS07

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)58
    • Downloads (Last 6 weeks)7
    Reflects downloads up to 26 Jan 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2023)FASTeller: A Hardware Partial Aggregator for Accurate Flow Counting in Cloud Networks2023 IEEE 31st International Conference on Network Protocols (ICNP)10.1109/ICNP59255.2023.10355603(1-12)Online publication date: 10-Oct-2023
    • (2023)Empirical Evaluation of DASH Streaming over Multipath TCP in a Mixed Traffic Environment2023 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS)10.1109/ANTS59832.2023.10468727(680-685)Online publication date: 17-Dec-2023
    • (2023)Impact of class imbalance in VeReMi dataset for misbehavior detection in autonomous vehiclesSoft Computing10.1007/s00500-023-08003-4Online publication date: 22-Mar-2023
    • (2022)Procedures, Criteria, and Machine Learning Techniques for Network Traffic Classification: A SurveyIEEE Access10.1109/ACCESS.2022.318113510(61135-61158)Online publication date: 2022
    • (2021)Network traffic classification for data fusion: A surveyInformation Fusion10.1016/j.inffus.2021.02.00972(22-47)Online publication date: Aug-2021
    • (2021)AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environmentConcurrency and Computation: Practice and Experience10.1002/cpe.649134:1Online publication date: 13-Jul-2021
    • (2020)Timely Classification and Verification of Network Traffic Using Gaussian Mixture ModelsIEEE Access10.1109/ACCESS.2020.29925568(91287-91302)Online publication date: 2020
    • (2019)Exploratory study on Class Imbalance and solutions for Network Traffic ClassificationNeurocomputing10.1016/j.neucom.2018.07.091343:C(100-119)Online publication date: 28-May-2019
    • (2019)Network Traffic Analysis for Android Malware DetectionHybrid Artificial Intelligent Systems10.1007/978-3-030-29859-3_40(468-479)Online publication date: 26-Aug-2019
    • (2016)WHOSA: Network Flow Classification Based on Windowed Higher-Order Statistical AnalysisIEICE Transactions on Communications10.1587/transcom.2015AMP0003E99.B:5(1024-1031)Online publication date: 2016
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Login options

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media