Machine learning based metaheuristic hybrids for S-box optimization

Autor: Dania Tamayo-Vera, Antonio Bolufé-Röhler
Rok vydání: 2020
Předmět:
Zdroj: Journal of Ambient Intelligence and Humanized Computing. 11:5139-5152
ISSN: 1868-5145
1868-5137
DOI: 10.1007/s12652-020-01829-y
Popis: Recent research has consistently shown that the concurrence between exploration and exploitation can significantly limit the effectiveness of exploration on heuristic search. This has led to the design of hybrid algorithms that separate both task and alleviate this limitation. Many of these hybrids are based on the Leaders and Followers metaheuristic, which is specifically designed to avoid this concurrence and achieve an unbiased exploration. In this paper we adapt Leaders and Followers to a combinatorial domain in order to optimize the non-linearity and transparency order of S-boxes. Hybrid algorithms are then presented using Hill-climbing to perform exploitation. Using machine learning techniques these hybrids are further improved by automatically identifying the optimum transition point between exploration and exploitation. The solutions found are among the best S-boxes reported in literature.
Databáze: OpenAIRE