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Reseach Article

Selecting GA Parameters for Intrusion Detection

by S. N. Pawar, R. S. Bichkar
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 6 - Number 7
Year of Publication: 2014
Authors: S. N. Pawar, R. S. Bichkar

S. N. Pawar, R. S. Bichkar . Selecting GA Parameters for Intrusion Detection. International Journal of Applied Information Systems. 6, 7 ( January 2014), 15-20. DOI=10.5120/ijais14-451078

@article{ 10.5120/ijais14-451078,
author = { S. N. Pawar, R. S. Bichkar },
title = { Selecting GA Parameters for Intrusion Detection },
journal = { International Journal of Applied Information Systems },
issue_date = { January 2014 },
volume = { 6 },
number = { 7 },
month = { January },
year = { 2014 },
issn = { 2249-0868 },
pages = { 15-20 },
numpages = {9},
url = { },
doi = { 10.5120/ijais14-451078 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-07-05T18:52:59.110807+05:30
%A S. N. Pawar
%A R. S. Bichkar
%T Selecting GA Parameters for Intrusion Detection
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 6
%N 7
%P 15-20
%D 2014
%I Foundation of Computer Science (FCS), NY, 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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Index Terms

Computer Science
Information Sciences


Genetic algorithm intrusion detection parameter selection crossover mutation selection.