• DocumentCode
    3579841
  • Title

    Optimization Algorithm Based on Genetic Support Vector Machine Model

  • Author

    Lan Li ; Shaobin Ma ; Yun Zhang

  • Author_Institution
    Dept. of Comput., Lanzhou Univ. of Arts & Sci., Lanzhou, China
  • Volume
    1
  • fYear
    2014
  • Firstpage
    307
  • Lastpage
    310
  • Abstract
    Aiming at the problem of low accuracy in intrusion detection system, this paper established a genetic support vector machine (SVM) model according to the features of genetic algorithm and support vector machine algorithm. The model firstly optimizes the support vector parameters according to genetic algorithm, then we build the intrusion detection model with support vector machine optimized and use the model to detect. The experiments choose the proper parameters (c, s) through discussing the influence of support vector machines parameters to the detection accuracy. The results show that putting genetic support vector machine model into intrusion detection improved detection accuracy.
  • Keywords
    genetic algorithms; security of data; support vector machines; genetic algorithm; genetic support vector machine model; intrusion detection system; optimization algorithm; Accuracy; Classification algorithms; Genetic algorithms; Genetics; Intrusion detection; Kernel; Support vector machines; genetic algorithms; genetic support vector machine model; intrusion detection; support vector machine;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2014 Seventh International Symposium on
  • Print_ISBN
    978-1-4799-7004-9
  • Type

    conf

  • DOI
    10.1109/ISCID.2014.99
  • Filename
    7064197