• DocumentCode
    3642077
  • Title

    An anomaly detection framework for BGP

  • Author

    Iñigo Ortiz de Urbina Cazenave;Erkan Köşlük;Murat Can Ganiz

  • Author_Institution
    University of the Basque Country, Alameda de Urquijo, 48013 Bilbao, Spain
  • fYear
    2011
  • fDate
    6/1/2011 12:00:00 AM
  • Firstpage
    107
  • Lastpage
    111
  • Abstract
    Abnormal events such as large scale power outages, misconfigurations, and worm attacks can affect the global routing infrastructure and consequently create regional or global Internet service interruptions. As a result, early detection of abnormal events is of critical importance. In this study we present a framework based on data mining algorithms that are applied to anomaly detection on global routing infrastructure. To show the applicability of our framework, we conduct extensive experiments with a variety of abnormal events and classification algorithms. Our results demonstrate that when we train our system with abnormal events including worm attacks, power supply outages, submarine cable cuts, and misconfigurations, we can detect a similar type of event as it happens.
  • Keywords
    "Routing","Grippers","Feature extraction","Internet","Data mining","Classification algorithms","Training"
  • Publisher
    ieee
  • Conference_Titel
    Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on
  • Print_ISBN
    978-1-61284-919-5
  • Type

    conf

  • DOI
    10.1109/INISTA.2011.5946083
  • Filename
    5946083