• DocumentCode
    3100935
  • Title

    Early warning and tracking technology against large-scaleflow attack in information network for ITS

  • Author

    Zhang, You-chun ; Liu, Zeng-liang ; Wei, Jun ; Ma, Fang

  • Author_Institution
    Inf. Eng. Inst., Univ. of Sci. & Technol. of Beijing, Beijing, China
  • Volume
    6
  • fYear
    2009
  • fDate
    12-15 July 2009
  • Firstpage
    3122
  • Lastpage
    3128
  • Abstract
    The critical security problem in ITS information network is network availability. It is important to avoid flow attack against network availability. This paper analyzes first several current IP-track technologies to find attack route, then analyzes and evaluates the ldquovector edge router samplingrdquo (RVES) method proposed by Chinese experts lately. We developed a prototype system based on RVES way, and carry on experiment in ITS network, results show that the RVES method is easy to implement and flexible for marking probability´s strategy configuration, which can effectively solve the early warning and detect problem about flow attack. Lastly, we proposed possible real world applications of RVES.
  • Keywords
    IP networks; information networks; telecommunication network routing; telecommunication security; IP-track technology; ITS information network; attack route; early warning technology; flow attack; network availability; security problem; tracking technology; vector edge router sampling; Availability; Cybernetics; Data security; Information security; Intelligent transportation systems; Large-scale systems; Machine learning; Packaging; Random number generation; Sampling methods; Flow attack; IP tracking; ITS network; PPM method; RVES method; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Conference_Location
    Baoding
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212709
  • Filename
    5212709