S. Ali, M. N. Qureshi, and A. G. Abbasi, Analysis of BYOD security frameworks, Conference on Information Assurance and Cyber Security (CIACS), pp.56-61, 2015.

M. De-arruda-pereira, E. G. Carrano, C. A. Junior, and J. A. De-vasconcelos, A comparative study of optimization models in genetic programming-based rule extraction problems, Soft Comput, vol.23, issue.4, pp.1179-1197, 2019.

T. Back, Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms, 1996.

J. A. Castellanos-garzón, J. Ramos, Y. M. Martín, J. F. Paz, and E. Costa, A genetic programming approach applied to feature selection from medical data

, Practical Applications of Computational Biology and Bioinformatics, 12th International Conference, vol.803, pp.200-207, 2018.

J. Derrac, S. García, D. Molina, and F. Herrera, A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms, Swarm and Evolutionary Computation, vol.1, issue.1, pp.3-18, 2011.

P. G. Espejo, S. Ventura, and F. Herrera, A survey on the application of genetic programming to classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.40, issue.2, pp.121-144, 2010.

I. D. Falco, A. D. Cioppa, and E. Tarantino, Discovering interesting classification rules with genetic programming, Applied Soft Computing, vol.1, issue.4, pp.257-269, 2002.

A. A. Freitas, Data mining and knowledge discovery with evolutionary algorithms, 2002.

P. García-sánchez, A. Fernández-ares, A. M. Mora, P. A. Valdivieso, J. González et al., Tree depth influence in genetic programming for generation of competitive agents for RTS games, Applications of Evolutionary Computation -17th European Conference, vol.8602, pp.411-421, 2014.

M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann et al., The WEKA data mining software: An update, SIGKDD Explorations, vol.11, issue.1, 2009.

N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study, Intelligent data analysis, vol.6, issue.5, pp.429-449, 2002.

M. Kaeo, Designing Network Security, Second Edition, 2003.

T. Pietraszek and A. Tanner, Data mining and machine learning -towards reducing false positives in intrusion detection, Information security technical report, vol.10, issue.3, pp.169-183, 2005.

L. Prechelt, PROBEN 1-a set of benchmarks and benchmarking rules for neural network training algorithms, 1994.

S. R. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics, vol.21, issue.3, pp.660-674, 1991.

A. Tsakonas, G. Dounias, J. Jantzen, H. Axer, B. Bjerregaard et al., Evolving rule-based systems in two medical domains using genetic programming, Artificial Intelligence in Medicine, vol.32, issue.3, pp.195-216, 2004.

I. H. Witten and E. Frank, Data Mining: Practical machine learning tools and techniques, 2005.