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Early Classification of Network Traffic through Multi-classification

机译:通过多分类对网络流量进行早期分类

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摘要

In this work we present and evaluate different automated combination techniques for traffic classification. We consider six intelligent combination algorithms applied to both traditional and more recent traffic classification techniques using either packet content or statistical properties of flows. Preliminary results show that, when selecting complementary classifiers, some combination algorithms allow a further improvement -in terms of classification accuracy - over already well-performing standalone classification techniques. Moreover, our experiments show that the positive impact of combination is particularly significant when there are early-classification constraints, that is, when the classification of a flow must be obtained in its early stage (e.g. first 1-4 packets) in order to perform network operations online.
机译:在这项工作中,我们介绍并评估用于流量分类的不同自动组合技术。我们考虑使用分组内容或流的统计属性将六种智能组合算法应用于传统和较新的流量分类技术。初步结果表明,在选择互补分类器时,相对于已经表现良好的独立分类技术,某些组合算法在分类精度方面可以进一步提高。而且,我们的实验表明,在存在早期分类约束时,即必须在早期(例如前1-4个数据包)中获得流分类时,组合的积极影响尤其重要。在线网络运营。

著录项

  • 来源
    《Traffic monitoring and analysis》|2011年|p.122-135|共14页
  • 会议地点 Vienna(AT);Vienna(AT)
  • 作者单位

    Department of Computer Engineering and Systems, Universita di Napoli Federico II;

    Department of Computer Engineering and Systems, Universita di Napoli Federico II;

    Department of Computer Engineering and Systems, Universita di Napoli Federico II;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 计算机网络;
  • 关键词

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