Abstract
The security policy rules in companies are generally proposed by the Chief Security Officer (CSO), who must, for instance, select by hand which access events are allowed and which ones should be forbidden. In this work we propose a way to automatically obtain rules that generalise these single-event based rules using Genetic Programming (GP), which, besides, should be able to present them in an understandable way. Our GP-based system obtains good dataset coverage and small ratios of false positives and negatives in the simulation results over real data, after testing different fitness functions and configurations in the way of coding the individuals.
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Acknowledgements
This work has been partially funded by projects RTI2018-102002-A-I00 (Ministerio de Ciencia, Innovación y Universidades), TIN2017-85727-C4-2-P (Ministerio español de Economía y Competitividad), and TEC2015-68752 (also funded by FEDER), as well as project B-TIC-402-UGR18 (FEDER y Junta de Andalucía).
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de las Cuevas, P., García-Sánchez, P., Chelly Dagdia, Z., García-Arenas, MI., Merelo Guervós, J.J. (2020). Automatic Rule Extraction from Access Rules Using Genetic Programming. In: Castillo, P.A., Jiménez Laredo, J.L., Fernández de Vega, F. (eds) Applications of Evolutionary Computation. EvoApplications 2020. Lecture Notes in Computer Science(), vol 12104. Springer, Cham. https://doi.org/10.1007/978-3-030-43722-0_4
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