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research-article

NeTraMark: a network traffic classification benchmark

Published: 22 January 2011 Publication History

Abstract

Recent research on Internet traffic classification has produced a number of approaches for distinguishing types of traffic. However, a rigorous comparison of such proposed algorithms still remains a challenge, since every proposal considers a different benchmark for its experimental evaluation. A lack of clear consensus on an objective and cientific way for comparing results has made researchers uncertain of fundamental as well as relative contributions and limitations of each proposal. In response to the growing necessity for an objective method of comparing traffic classifiers and to shed light on scientifically grounded traffic classification research, we introduce an Internet traffic classification benchmark tool, NeTraMark. Based on six design guidelines (Comparability, Reproducibility, Efficiency, Extensibility, Synergy, and Flexibility/Ease-of-use), NeTraMark is the first Internet traffic lassification benchmark where eleven different state-of-the-art traffic classifiers are integrated. NeTraMark allows researchers and practitioners to easily extend it with new classification algorithms and compare them with other built-in classifiers, in terms of three categories of performance metrics: per-whole-trace flow accuracy, per-application flow accuracy, and computational performance.

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    Published In

    cover image ACM SIGCOMM Computer Communication Review
    ACM SIGCOMM Computer Communication Review  Volume 41, Issue 1
    January 2011
    132 pages
    ISSN:0146-4833
    DOI:10.1145/1925861
    Issue’s Table of Contents

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 22 January 2011
    Published in SIGCOMM-CCR Volume 41, Issue 1

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    Author Tags

    1. benchmark
    2. traffic classification

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    • (2024)STI: A self-evolutive traffic identification system for unknown applications based on improved random forestComputer Communications10.1016/j.comcom.2024.02.010219(64-75)Online publication date: Apr-2024
    • (2021)A Network Traffic Processing Library for ICS Anomaly Detection7th Conference on the Engineering of Computer Based Systems10.1145/3459960.3459963(1-7)Online publication date: 26-May-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)HEDGE: Efficient Traffic Classification of Encrypted and Compressed PacketsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2019.291115614:11(2916-2926)Online publication date: Nov-2019
    • (2019)A New Approach to Multivariate Network Traffic AnalysisJournal of Computer Science and Technology10.1007/s11390-019-1915-y34:2(388-402)Online publication date: 22-Mar-2019
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    • (2017)PosterProceedings of the 23rd Annual International Conference on Mobile Computing and Networking10.1145/3117811.3131254(531-533)Online publication date: 4-Oct-2017
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