Human Behaviour Based Optimization Supported with Self-Organizing Maps for Solving the S-Box Design Problem
Keywords: Cryptography; metaheuristics; self, organizing maps; substitution box
Abstract
The cryptanalytic resistance of modern block and stream encryption systems mainly depends on the substitution box (S-box). In this context, the problem is thus to create an S-box with higher value of nonlinearity because this property can provide some degree of protection against linear and differential cryptanalysis attacks. In this paper, we design a scheme built on a human behavior-based optimization algorithm, supported with Self-Organizing Maps to prevent premature convergence and improve the nonlinearity property in order to obtain strong 8 \times 8 substitution boxes. The experiments are compared with S-boxes obtained using other metaheuristic algorithms such as Ant Colony Optimization, Genetic Algorithm and an approach based on chaotic functions and show that the obtained S-boxes have good cryptographic properties. The obtained S-box is investigated against standard tests such as bijectivity, nonlinearity, strict avalanche criterion, bit independence criterion, linear probability and differential probability, proving that the proposed scheme is proficient to discover a strong nonlinear component of encryption systems.
Más información
| Título según SCOPUS: | Human Behaviour Based Optimization Supported with Self-Organizing Maps for Solving the S-Box Design Problem |
| Título de la Revista: | IEEE Access |
| Volumen: | 9 |
| Editorial: | Institute of Electrical and Electronics Engineers Inc. |
| Fecha de publicación: | 2021 |
| Página final: | 84618 |
| Idioma: | English |
| DOI: |
10.1109/ACCESS.2021.3087139 |
| Notas: | SCOPUS |