Modified genetic algorithms for generating S-boxes with high nonlinearity
DOI:
https://doi.org/10.30837/rt.2024.2.217.08Keywords:
genetic algorithm, S-box, cryptography, nonlinearity, Walsh-Hadamard Spectrum, selection, mutation, cryptanalysis, optimization, data securityAbstract
This article discusses the use of modified genetic algorithms to generate S-boxes with high nonlinearity, which is critical for ensuring the security of cryptographic algorithms. S-boxes play a key role in providing resistance to cryptanalysis, in particular to linear and differential cryptanalysis attacks. In the course of the study, a series of experiments were conducted using a genetic algorithm modified by additional storage of the current population and the use of selection. This approach has significantly improved the efficiency of the algorithm compared to the classical genetic algorithm. The best results were achieved with the minimum number of instances in the population and the optimal number of mutations. It was found that the algorithm finds S-blocks with a probability of 99% with a nonlinearity of 104, which demonstrates its high efficiency. The results of the study showed that the modified algorithm is able to provide stable generation of S-boxes with the required attack resistance properties. The proposed method also proved to be flexible in parameter settings, which allows it to be adapted for various cryptographic applications. The paper also discusses the possibilities of further improving the algorithm, including the study of other mutation and selection methods, as well as parameter optimization to achieve even better results. Additionally, the possibility of using distributed computing to increase the speed of S-boxes generation is considered. The results of the study indicate the prospects of the proposed approach for creating attack-resistant cryptographic systems.
This study considered the use of genetic algorithms to generate S-boxes with high nonlinearity. The proposed approach was based on the use of an objective function with optimal parameters, which allowed achieving high efficiency in generating bioactive S-blocks.
The proposed algorithm showed high stability and efficiency in generating S-boxes with nonlinearity, which is confirmed by a 99% probability of achieving the goal. These results indicate the prospects of using genetic algorithms in cryptographic applications that require high resistance to attacks.
In future research, it is planned to further improve the algorithm by exploring other methods of mutation and selection, as well as optimizing parameters to achieve even better results. In addition, it is possible to use distributed computing to further speed up the process of generating S-boxes.
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