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
Link To Document :
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