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Traffic Modeling and Classification Using Packet Train Length and Packet Train Size

  • Dinil Mon Divakaran
  • Hema A. Murthy
  • Timothy A. Gonsalves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4268)

Abstract

Traffic modeling and classification finds importance in many areas such as bandwidth management, traffic analysis, traffic prediction, network planning, Quality of Service provisioning and anomalous traffic detection. Network traffic exhibits some statistically invariant properties. Earlier works show that it is possible to identify traffic based on its statistical characteristics. In this paper, an attempt is made to identify the statistically invariant properties of different traffic classes using multiple parameters, namely packet train length and packet train size. Models generated using these parameters are found to be highly accurate in classifying different traffic classes. The parameters are also useful in revealing different classes of services within different traffic classes.

Keywords

Gaussian Mixture Model Vector Quantization Internet Service Provider Traffic Class Packet Header 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dinil Mon Divakaran
    • 1
  • Hema A. Murthy
    • 1
  • Timothy A. Gonsalves
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology, MadrasChennai

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