Crossover grey wolf optimizer and discrete chaotic map for substitution box design and optimization
Yükleniyor...
Dosyalar
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The efficiency of a cryptographical scheme is the effective interplay between its various
components. Among these components, the substitution box, also known as the S-box, plays
a crucial role in establishing the robustness of the cryptographic system. The S-box plays a
crucial role in bolstering the system's resistance against various cryptanalytic attacks,
making it an essential component. Extensive investigate in this field has been conducted
since the late 1980s, aiming to further enhance the security provided by S-boxes against both
existing and potential attacks. Consequently, the creation of appropriate S-boxes has
garnered considerable attention within the cryptography community. As S-boxes can
manifest different combinations of these properties, designing a cryptographically robust Sbox often involves striking a balance among these properties during optimization. Numerous
designs-based S-boxes have been put forward in the literature, with metaheuristic-based
approaches gaining popularity. However, no individual metaheuristic method can assert
superiority over others as an ultimate solution. Hence, the pursuit of novel metaheuristicbased methods for S-box generation remains pertinent.
This study endeavours to introduce a novel 8 × 8 S-boxes design based on the grey wolf
optimizer (GWO), which is a recently developed metaheuristic algorithm inspired by the
hunting behaviour of grey wolves. which is a recently developed metaheuristic algorithm
inspired by the hunting behaviour of grey wolves to enhance the generated S-boxes based
on the standard grey wolf optimizer (GWO), two variations of the GWO are proposed. The first variation, known as the chaotic grey wolf optimizer (CGWO), employs a discrete
chaotic mapping technique for initialization to ensure the search commences from
favourable positions. The second variation, named the crossover grey wolf optimizer
(XGWO), generates new solutions by combining the previously discovered best solutions
(Alpha and Beta) in a specific order. This novel crossover step guarantees the global search
capability of the algorithm and enhances the search performance of GWO, even when the
stop condition is not met.
Açıklama
Anahtar Kelimeler
Substitution Boxes, Optimization, Nature-Inspired Algorithms, Grey Wolf Optimizer, Cryptology, Discrete Chaotic Map
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Lawah, A. I. (2023). Crossover grey wolf optimizer and discrete chaotic map for substitution box design and optimization. (Yayınlanmamış doktora tezi). Altınbaş Üniversitesi, Lisansüstü Eğitim Enstitüsü, İstanbul.
