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Traffic classification on the fly

Published: 28 April 2006 Publication History

Abstract

The early detection of applications associated with TCP flows is an essential step for network security and traffic engineering. The classic way to identify flows, i.e. looking at port numbers, is not effective anymore. On the other hand, state-of-the-art techniques cannot determine the application before the end of the TCP flow. In this editorial, we propose a technique that relies on the observation of the first five packets of a TCP connection to identify the application. This result opens a range of new possibilities for online traffic classification.

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  • (2024)Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNsSensors10.3390/s2403096324:3(963)Online publication date: 1-Feb-2024
  • (2024)Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric VisibilityFuture Internet10.3390/fi1607022316:7(223)Online publication date: 25-Jun-2024
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Published In

cover image ACM SIGCOMM Computer Communication Review
ACM SIGCOMM Computer Communication Review  Volume 36, Issue 2
April 2006
57 pages
ISSN:0146-4833
DOI:10.1145/1129582
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 April 2006
Published in SIGCOMM-CCR Volume 36, Issue 2

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Author Tags

  1. applications
  2. machine learning
  3. traffic classification

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Cited By

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  • (2025)Data Augmentation-Based Enhancement for Efficient Network Traffic ClassificationIEEE Access10.1109/ACCESS.2024.352500013(6006-6028)Online publication date: 2025
  • (2024)Explainable Learning-Based Timeout Optimization for Accurate and Efficient Elephant Flow Prediction in SDNsSensors10.3390/s2403096324:3(963)Online publication date: 1-Feb-2024
  • (2024)Transforming Network Management: Intent-Based Flexible Control Empowered by Efficient Flow-Centric VisibilityFuture Internet10.3390/fi1607022316:7(223)Online publication date: 25-Jun-2024
  • (2024)HClassJournal of High Speed Networks10.3233/JHS-23014530:4(517-533)Online publication date: 15-Oct-2024
  • (2024)Benchmarking Class Incremental Learning in Deep Learning Traffic ClassificationIEEE Transactions on Network and Service Management10.1109/TNSM.2023.328743021:1(51-69)Online publication date: 1-Feb-2024
  • (2024)A Framework for Classifying Applications from Raw Network Traffic TracesSoutheastCon 202410.1109/SoutheastCon52093.2024.10500148(1006-1015)Online publication date: 15-Mar-2024
  • (2024)Encrypted Traffic Classification at Line Rate in Programmable Switches with Machine LearningNOMS 2024-2024 IEEE Network Operations and Management Symposium10.1109/NOMS59830.2024.10575394(1-9)Online publication date: 6-May-2024
  • (2024)A Survey of Encrypted Traffic Classification: Datasets, Representation, Approaches and Future Thinking2024 IEEE/ACIS 24th International Conference on Computer and Information Science (ICIS)10.1109/ICIS61260.2024.10778376(113-120)Online publication date: 20-Sep-2024
  • (2024)Accurate and efficient elephant-flow classification based on co-trained models in evolved software-defined networksDigital Communications and Networks10.1016/j.dcan.2024.10.017Online publication date: Oct-2024
  • (2024)Network traffic classification: Techniques, datasets, and challengesDigital Communications and Networks10.1016/j.dcan.2022.09.00910:3(676-692)Online publication date: Jun-2024
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