• DocumentCode
    3501956
  • Title

    A quantitative model for network security situation awareness based on immunity and grey theory

  • Author

    Shi, Yuanquan ; Li, Tao ; Chen, Wen ; Zhang, Ruirui

  • Author_Institution
    Sch. of Comput. Sci., Sichuan Univ., Chengdu, China
  • Volume
    4
  • fYear
    2009
  • fDate
    8-9 Aug. 2009
  • Firstpage
    14
  • Lastpage
    18
  • Abstract
    To effectively evaluate and predict network security situation, a quantitative model for network security situation awareness based on artificial immune system and grey theory is proposed. In this model, the formal definitions of self, non-self, antigen and detector is given. According to the relationship between the antibody-concentration of memory detector and the attack intensity of network, network security situation evaluation sub-model based on artificial immune system is given. And to forecast the attack intensity that the current network faces in the next step, network situation predication sub-model based on grey theory is given. Experimental results exhibit that the proposed model provides a novel approach for network security situation awareness, and holds better characters of self-adaptability and real-time processing.
  • Keywords
    artificial immune systems; security of data; self-adjusting systems; artificial immune system; grey theory; network security situation awareness; quantitative model; real-time processing; self-adaptability; Artificial immune systems; Biological system modeling; Computer networks; Computer security; Detectors; Immune system; Information security; Predictive models; Stability; Surveillance; Artificial Immune System; Grey Prediction; Situation Awareness; Situation Evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-4247-8
  • Type

    conf

  • DOI
    10.1109/CCCM.2009.5267847
  • Filename
    5267847