• DocumentCode
    1935943
  • Title

    Quantification of Network Security Situational Awareness Based on Evolutionary Neural Network

  • Author

    Liang, Ying ; Wang, Hui-Qiang ; Lai, Ji-Bao

  • Author_Institution
    Harbin Eng. Univ., Harbin
  • Volume
    6
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    3267
  • Lastpage
    3272
  • Abstract
    The proposal of network security situational awareness (NSSA) research means a breakthrough and an innovation to the traditional network security technologies, and it has become a new hot research topic in network security field. Combined with evolutionary strategy and neural network, a quantitative method of network security situational awareness is proposed in this paper. Evolutionary strategy is used to optimize the parameters of neural network, and then the evolutionary neural network model is established to extract the network security situational factors, so the quantification of network security situation is achieved. Finally simulated experiment is done to validate that the evolutionary neural network model can extract situational factors and the model has better generalization ability, which supports the network security technical technologies greatly.
  • Keywords
    evolutionary computation; neural nets; optimisation; telecommunication security; evolutionary strategy; network security; neural network; optimization; situational awareness; situational factors; Computer science; Computer security; Cybernetics; Electronic mail; Fault detection; Information security; Machine learning; Neural networks; Proposals; Technological innovation; Evolutionary strategy; Network security; Neural network; Situational awareness; Situational factor;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370711
  • Filename
    4370711