• DocumentCode
    623964
  • Title

    Botnet detection revisited: Theory and practice of finding malicious P2P networks via Internet connection graphs

  • Author

    Ruehrup, Stefan ; Urbano, Pierfrancesco ; Berger, A. ; D´Alconzo, Alessandro

  • Author_Institution
    FTW - Telecommun. Res. Center Vienna, Vienna, Austria
  • fYear
    2013
  • fDate
    14-19 April 2013
  • Firstpage
    3393
  • Lastpage
    3398
  • Abstract
    In this paper we review state-of-the-art botnet detection algorithms that reveal the control traffic of malicious peer-topeer (P2P) networks by targeting topological properties of their interconnectivity graph. This class of detection methods does not rely on the exchanged content and therefore is also applicable to encrypted control traffic. However, in practice, an ISP monitoring customer traffic over an edge router will usually see only a fraction of the overall botnet, thus restricting the available bot connectivity information and limiting the applicability of general community detection approaches. In this paper we critically review graph based detection methods suitable for edge router monitoring using two types of real network traces. We show experimentally that using meta-graphs of mutual contacts proposed by Coskun et al. 2010 has the highest potential on result quality. We improve this approach by presenting a computationally less complex algorithm with similar result quality. Furthermore we explain ways to alleviate the cost of dealing with false positives in the result set.
  • Keywords
    computer network security; graph theory; peer-to-peer computing; ISP; Internet connection graphs; bot connectivity information; botnet detection algorithm; customer traffic; edge router monitoring; graph based detection methods; interconnectivity graph; malicious P2P networks; malicious peer-topeer networks; Clustering algorithms; Communities; DSL; Dispersion; Monitoring; Peer-to-peer computing; Topology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    INFOCOM, 2013 Proceedings IEEE
  • Conference_Location
    Turin
  • ISSN
    0743-166X
  • Print_ISBN
    978-1-4673-5944-3
  • Type

    conf

  • DOI
    10.1109/INFCOM.2013.6567170
  • Filename
    6567170