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Selecting GA Parameters for Intrusion Detection

S. N. Pawar, R. S. Bichkar Published in Security

International Journal of Applied Information Systems
Year of Publication: 2014
© 2013 by IJAIS Journal
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  1. S N Pawar and R S Bichkar. Article: Selecting GA Parameters for Intrusion Detection. International Journal of Applied Information Systems 6(7):15-20, January 2014. BibTeX

    	author = "S. N. Pawar and R. S. Bichkar",
    	title = "Article: Selecting GA Parameters for Intrusion Detection",
    	journal = "International Journal of Applied Information Systems",
    	year = 2014,
    	volume = 6,
    	number = 7,
    	pages = "15-20",
    	month = "January",
    	note = "Published by Foundation of Computer Science, New York, USA"


Genetic algorithms happen to be one of the preferred techniques for intrusion detection. It needs careful selection of its parameters like population size, number of generations, mutation rate, crossover rate, selection type etc. and also requires selecting appropriate percentage of attack samples in a data set to be able to find good solutions. Choosing unsuitable parameters and methods might result into longer program runs or even bad optimization results. In the proposed method, genetic algorithm is used for intrusion detection rule generation. It is implemented and run using different configurations and results are compared. Then the best GA parameters are selected for intrusion detection.


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Genetic algorithm, intrusion detection, parameter selection, crossover, mutation, selection.