• DocumentCode
    175394
  • Title

    Three Statistical Approaches to Sessionizing Network Flow Data

  • Author

    Rubin-Delanchy, Patrick ; Lawson, Daniel J. ; Turcotte, Melissa J. ; Heard, Nick ; Adams, Niall M.

  • Author_Institution
    Sch. of Math., Univ. of Bristol, Bristol, UK
  • fYear
    2014
  • fDate
    24-26 Sept. 2014
  • Firstpage
    244
  • Lastpage
    247
  • Abstract
    The network traffic generated by a computer, or a pair of computers, is often well modelled as a series of sessions. These are, roughly speaking, intervals of time during which a computer is engaging in the same, continued, activity. This article explores a variety of statistical approaches to re-discovering sessions from network flow data using timing alone. Solutions to this problem are essential for network monitoring and cyber-security. For example overlapping sessions on a computer network can be evidence of an intruder ´tunnelling´.
  • Keywords
    computer network management; computer network reliability; computer network security; computers; cyber security; intruder tunnelling; network flow data; network monitoring; network traffic; statistical approaches; Bayes methods; Computational modeling; Computers; Data models; Educational institutions; Mathematical model; Stochastic processes; clustering; network flow; point process; sessionization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligence and Security Informatics Conference (JISIC), 2014 IEEE Joint
  • Conference_Location
    The Hague
  • Print_ISBN
    978-1-4799-6363-8
  • Type

    conf

  • DOI
    10.1109/JISIC.2014.46
  • Filename
    6975583