Crossover grey wolf optimizer and discrete chaotic map for substitution box design and optimization
| dc.contributor.advisor | Ibrahim, Abdullahi Abdu | |
| dc.contributor.author | Lawah, Ali Ibrahim | |
| dc.date.accessioned | 2024-01-13T12:45:15Z | |
| dc.date.available | 2024-01-13T12:45:15Z | |
| dc.date.issued | 2023 | en_US |
| dc.date.submitted | 2023 | |
| dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı | en_US |
| dc.description.abstract | 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. | en_US |
| dc.identifier.citation | 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. | en_US |
| dc.identifier.uri | https://hdl.handle.net/20.500.12939/4542 | |
| dc.identifier.yoktezid | 830969 | |
| dc.institutionauthor | Lawah, Ali Ibrahim | |
| dc.language.iso | en | |
| dc.publisher | Altınbaş Üniversitesi / Lisansüstü Eğitim Enstitüsü | en_US |
| dc.relation.publicationcategory | Tez | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Substitution Boxes | en_US |
| dc.subject | Optimization | en_US |
| dc.subject | Nature-Inspired Algorithms | en_US |
| dc.subject | Grey Wolf Optimizer | en_US |
| dc.subject | Cryptology | en_US |
| dc.subject | Discrete Chaotic Map | en_US |
| dc.title | Crossover grey wolf optimizer and discrete chaotic map for substitution box design and optimization | |
| dc.type | Doctoral Thesis |
