Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


March Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the March 2023 Edition of the journal. The last date of research paper submission is February 15, 2023.

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
Download full text
  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.


  1. Ko, Calvin, M. Ruschitzka and K. Levitt, "Execution monitoring of security-critical programs in distributed systems: A specification-based approach", Security and Privacy, Proceedings. IEEE symposium. IEEE, 1997.
  2. S. Owais, V. Snasel and P. Kromer, A. Abraham, "Survey Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques", 7th Computer Information Systems and Industrial Management Applications, 2008, IEEE press, June 2008, pp. 300-307, DOI 10. 1109/CISIM. 2008. 49.
  3. Y. Wang, D. Gu, X. Tian and J. Li, "Genetic Algorithm Rule Definition for Denial of Services Network Intrusion Detection", International Conference on Computational Intelligence and Natural Computing, IEEE, 2009, pp. 99-102.
  4. R. H. Gong, M. Zulkernine, P. Abolmaesumi, "A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection", SNPD/ SAWN' 05, IEEE, 2005.
  5. S. Mukkamala and A. H. Sung, "A Comparative Study of Techniques for Intrusion Detection", Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'03), IEEE, 2003.
  6. S. Ashfaq, M. U. Farooq and A. Karim, "Efficient Rule Generation for Cost-Sensitive Misuse Detection Using Genetic Algorithms," IEEE, 2006.
  7. T. Xia, G. Qu, S. Hariri and M. Yousif, "An efficient Network Intrusion Detection Method Based on Information Theory and Genetic Algorithm", IEEE, 2005.
  8. M. Middlemiss and G. Dick, "Weighted Feature Extraction Using a Genetic Algorithm for Intrusion Detection", 2003 Congress on Evolutionary Computation (cec-03) 2003, pp. 1669-1675.
  9. C. H. Lee, S. W. Shin and J. W. Chung, "Network Intrusion Detection through Genetic Feature Selection", SNPD, IEEE, 2006.
  10. B. Mukherjee, L. T. Herberlein and K. N. Levitt, "Network Intrusion Detection", IEEE Network, 8(3):26-41, May/June 1994.
  11. MIT Lincoln Laboratory, DARPA datasets, MIT, USA, http://www. ll. mit. edu/mission/communications /ist/ corpora/ideval/data/1998data. html
  12. S. N. Pawar and R. S. Bichkar, "Using Enumeration in a GA based Intrusion Detection", International Journal of Computer Applications (IJCA), October- 2012.


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