• DocumentCode
    3110357
  • Title

    Intrusion Detection via Fuzzy-Genetic Algorithm Combination with Evolutionary Algorithms

  • Author

    Haghighat, A.T. ; Esmaeih, M. ; Saremi, A. ; Mousavi, V.R.

  • Author_Institution
    Shahid Beheshti Univ., Tehran
  • fYear
    2007
  • fDate
    11-13 July 2007
  • Firstpage
    587
  • Lastpage
    591
  • Abstract
    In this paper with the use of fuzzy genetic algorithm combination with evolutionary algorithms, as a method for local searching, it has been tried to exploit high capabilities of genetic algorithm, as a search algorithm, beside to other evolutionary algorithms, as local search algorithms, in order to increase efficiency of a rule learning system. For this purpose three hybrid algorithms have been used for solving the intrusion detection problem. These three algorithms are combination of genetic algorithm and SFL and PSO as three evolutionary algorithms which try to introduce efficient solutions for complex optimization problems by patterning from natural treatments.
  • Keywords
    computer networks; fuzzy reasoning; fuzzy set theory; genetic algorithms; particle swarm optimisation; search problems; telecommunication security; PSO; computer network; evolutionary algorithm; fuzzy-genetic algorithm; intrusion detection; local search method; particle swarm optimisation; rule learning system; Data mining; Evolutionary computation; Fuzzy systems; Genetic algorithms; Genetic engineering; Intrusion detection; Iterative algorithms; Learning systems; Local area networks; TCPIP;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer and Information Science, 2007. ICIS 2007. 6th IEEE/ACIS International Conference on
  • Conference_Location
    Melbourne, Qld.
  • Print_ISBN
    0-7695-2841-4
  • Type

    conf

  • DOI
    10.1109/ICIS.2007.124
  • Filename
    4276445