Abstract
In any corporation and organizations, the owner wants to introduce a best and efficient security solution with low cost and wants to get the high efficiency. In this paper, we suggest a method to select the best security solution among various security solutions using multi-objective genetic algorithm that considers the trade-off between cost and security. The designed system can support the best security solution from various aspects of security concerns. We use NSGA-II algorithm that is verified in various fields, and provide comparison results with the existing genetic algorithm.
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References
Chai, S.W.: Economic effects of personal information protection. Korea Consum. Agency 33, 43–64 (2008)
Kwon, Y.O., Kim, B.D.: The effect of information security breach and security investment announcement on the market value of Korean firms. Inf. Syst. Rev. 9(1), 105–120 (2007)
Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)
Horn, J., Nafpliotis, N., Golberg, D.: A niched Pareto genetic algorithm for multiobjective optimization. In: Proceedings of 1st IEEE Conference on Evolutionary Computation, vol. 1, pp. 82–87 (1994)
Yoon, J., Lee, J., Kim, D.: Feature selection in multi-label classification using NSGA-II algorithm. J. KIISE Softw. Appl. 40(3), 133–140 (2013)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Khu, S.T., Madsen, H.: Multi-objective calibration with Pareto preference ordering: an application to rainfall-runoff model calibration. Water Resour. Res. 41(3) (2005). http://onlinelibrary.wiley.com/doi/10.1029/2004WR003041/full
Park, J., Bang, Y., Lee, G., Nam, K.: Generation of security measure by using simple genetic algorithm. Proc. KIISE Conf. 30(21), 769–771 (2003)
Kalyanmoy, D., Amarendra, K.: Real-coded genetic algorithms with simulated binary crossover: studies on multimodel and multiobjective problems. Complex Syst. 9(6), 431–454 (1995)
Hamdan, M.: A dynamic polynomial mutation for eolutionary multi-objective optimization algorithms. Int. J. Artif. Intell. Tools 20(01), 209–219 (2011)
Deb, K., Deb, D.: Analysing mutation schemes for real-parameter genetic algorithms. Int. J. Artif. Intell. Soft Comput. 4(1), 1–28 (2014)
Acknowledgement
Following are results of a study on the “Leaders in INdustry-university Cooperation” Project, supported by the Ministry of Education, Science & Technology (MEST).
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Lee, Y., Jung, J., Ahn, C.W. (2016). Design of Selecting Security Solution Using Multi-objective Genetic Algorithm. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 681. Springer, Singapore. https://doi.org/10.1007/978-981-10-3611-8_48
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DOI: https://doi.org/10.1007/978-981-10-3611-8_48
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