• DocumentCode
    2957516
  • Title

    Modeling network attacks for scenario construction

  • Author

    Al-Mamory, Safaa O. ; Hongli Zhang ; Abbas, Ayad R.

  • Author_Institution
    Harbin Inst. of Technol., Harbin
  • fYear
    2008
  • fDate
    1-8 June 2008
  • Firstpage
    1495
  • Lastpage
    1502
  • Abstract
    The Intrusion detection system (IDS) is a security technology that attempts to identify network intrusions. Defending against multistep intrusions which prepare for each other is a challenging task. In this paper, the Context-Free Grammar (CFG) was used to describe the multistep attacks using alerts classes. Based on the CFGs, the modified LR parser was employed to generate the parse trees of the scenarios presented in the alerts. Instead of searching all the received alerts for those that prepare for a new alert, we only search for the latest alertpsilas type of each scenario. Consequently, the proposed system has an attractive time complexity. The experiments were performed on two different sets of network traffic traces, using different open-source and commercial IDSs. The detected scenarios are represented by Correlation Graphs (CGs). The experimental results show that the CFG can describe multistep attacks explicitly and the modified LR parser, based on the CFG, can construct scenarios successfully.
  • Keywords
    computational complexity; context-free grammars; security of data; tree searching; alert class; context-free grammar; correlation graph; intrusion detection system; modified LR parser; network attack modeling; scenario construction; security technology; time complexity; tree searching; Neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1098-7576
  • Print_ISBN
    978-1-4244-1820-6
  • Electronic_ISBN
    1098-7576
  • Type

    conf

  • DOI
    10.1109/IJCNN.2008.4633994
  • Filename
    4633994