Research Commons
      • Browse 
        • Communities & Collections
        • Titles
        • Authors
        • By Issue Date
        • Subjects
        • Types
        • Series
      • Help 
        • About
        • Collection Policy
        • OA Mandate Guidelines
        • Guidelines FAQ
        • Contact Us
      • My Account 
        • Sign In
        • Register
      View Item 
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computing and Mathematical Sciences Papers
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.

      Flow clustering using machine learning techniques

      McGregor, Anthony James; Hall, Mark A.; Lorier, Perry; Brunskill, James
      Thumbnail
      Files
      Flow_Clustering_Using_Machine_Learning_Techniques.pdf
      Accepted version, 284.6Kb
      DOI
       10.1007/978-3-540-24668-8_21
      Find in your library  
      Citation
      Export citation
      McGregor, A. J., Hall, M. A., Lorier, P., & Brunskill, J. (2004). Flow clustering using machine learning techniques. In C. Barakat & I. Pratt (Eds.), Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science (Vol. 3015, pp. 205–214). Springer, Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-540-24668-8_21
      Permanent Research Commons link: https://hdl.handle.net/10289/10848
      Abstract
      Packet header traces are widely used in network analysis. Header traces are the aggregate of traffic from many concurrent applications. We present a methodology, based on machine learning, that can break the trace down into clusters of traffic where each cluster has different traffic characteristics. Typical clusters include bulk transfer, single and multiple transactions and interactive traffic, amongst others. The paper includes a description of the methodology, a visualisation of the attribute statistics that aids in recognising cluster types and a discussion of the stability and effectiveness of the methodology.
      Date
      2004
      Type
      Conference Contribution
      Publisher
      Springer
      Rights
      This is an author’s accepted version of an article published in Passive and Active Network Measurement. PAM 2004. Lecture Notes in Computer Science, LNCS 3015. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-24668-8_21. © Springer, Berlin, Heidelberg 2004
      Collections
      • Computing and Mathematical Sciences Papers [1455]
      Show full item record  

      Usage

      Downloads, last 12 months
      663
       
       
       

      Usage Statistics

      For this itemFor all of Research Commons

      The University of Waikato - Te Whare Wānanga o WaikatoFeedback and RequestsCopyright and Legal Statement