Corpus ID: 56895360

How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets

@article{Rezaei2019HowTA,
  title={How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets},
  author={Shahbaz Rezaei and Xin Liu},
  journal={ArXiv},
  year={2019},
  volume={abs/1812.09761}
}
  • Shahbaz Rezaei, Xin Liu
  • Published 2019
  • Computer Science
  • ArXiv
    • Network traffic classification, which has numerous applications from security to billing and network provisioning, has become a cornerstone of today's computer networks. Previous studies have developed traffic classification techniques using classical machine learning algorithms and deep learning methods when large quantities of labeled data are available. However, capturing large labeled datasets is a cumbersome and time-consuming process. In this paper, we propose a semi-supervised approach… CONTINUE READING
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    10 Citations
    Highly Influencial Citations
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    Background Citations
    5
    Methods Citations
    4
    Results Citations
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    10 Citations
    Citation Type

    Citation Type

    Multitask Learning for Network Traffic Classification
    • Shahbaz Rezaei, X. Liu
    • Computer Science, Mathematics
    • 2020 29th International Conference on Computer Communications and Networks (ICCCN)
    • 2020
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    Deep Learning for Encrypted Traffic Classification: An Overview
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    Adversarial Network Traffic: Toward Evaluating the Robustness of Deep Learning Based Network Traffic Classification
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    A Survey of Techniques for Mobile Service Encrypted Traffic Classification Using Deep Learning
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    Large-Scale Mobile App Identification Using Deep Learning
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    Robust Network Flow Classification against Malicious Feature Manipulation
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