• DocumentCode
    1688245
  • Title

    Highlights on analyzing one-way traffic using different tools

  • Author

    Balkanli, Eray ; Zincir-Heywood, A. Nur

  • Author_Institution
    Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS, Canada
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In this paper, we present our analysis using four different systems on two different one-way network traffic data sets. Specifically, we have explored the usage of two network traffic analyzers, namely Corsaro and Cisco ASA 5515-X, and two machine learning based systems, namely the C4.5 Decision Tree classifier and the AdaBoost.M1 classifier. We have employed these four systems on two publicly available one-way network data sets provided by CAIDA from 2008 and 2012. Our analysis on these systems are based on the detection rate, false alarm rate, computational cost and ease of use of these systems. To the best of our knowledge, this work is the first one performing such an analysis and evaluating machine learning based systems against well known commercial as well as open source ones on one-way network traffic data sets.
  • Keywords
    computer network security; decision trees; learning (artificial intelligence); telecommunication traffic; AdaBoost.M1 classifier; C4.5 decision tree classifier; CAIDA; Cisco ASA 5515-X; different tools; false alarm rate; machine learning; network traffic data sets; one-way network traffic data sets; one-way traffic analysis; Backscatter; Decision trees; IP networks; Monitoring; Protocols; Security; Training; One-way traffic; machine learning; network traffic monitoring;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence for Security and Defense Applications (CISDA), 2015 IEEE Symposium on
  • Conference_Location
    Verona, NY
  • Print_ISBN
    978-1-4673-7556-6
  • Type

    conf

  • DOI
    10.1109/CISDA.2015.7208635
  • Filename
    7208635