Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization
A metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-for...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10103220/ |
| _version_ | 1797805889391951872 |
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| author | Ali Ibrahim Lawah Abdullahi Abdu Ibrahim Sinan Q. Salih Hussam S. Alhadawi Poh Soon JosephNg |
| author_facet | Ali Ibrahim Lawah Abdullahi Abdu Ibrahim Sinan Q. Salih Hussam S. Alhadawi Poh Soon JosephNg |
| author_sort | Ali Ibrahim Lawah |
| collection | DOAJ |
| description | A metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> substitution boxes (S-boxes). The GWO was developed as a novel metaheuristic based on inspiration from grey wolves and how they hunt. The ability of the GWO to quickly explore the search space for the near/optimal feature subsets that maximize any given fitness function (in consideration of its distinctive hierarchical structure) aids in the construction of strong S-boxes that can satisfy the required criteria. However, when tackling optimization problems, GWO may experience the problem of premature convergence. Therefore, a variant of GWO called Crossover Grey Wolf Optimizer (XGWO) has been proposed in this study. The performance of the proposed novel approach was evaluated using numerous cryptographic performance metrics, including bijective property, bit independence, strict avalanche, linear probability, and I/O XOR distribution and the result was contrasted with a couple of existing S-box creation techniques. Overall, the results of the experiment showed that the suggested S-box design had adequate cryptographic features. |
| first_indexed | 2024-03-13T05:58:57Z |
| format | Article |
| id | doaj.art-66c34cbe8b31467f80bc3e4729135b43 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T05:58:57Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-66c34cbe8b31467f80bc3e4729135b432023-06-12T23:02:14ZengIEEEIEEE Access2169-35362023-01-0111424164243010.1109/ACCESS.2023.326629010103220Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and OptimizationAli Ibrahim Lawah0https://orcid.org/0000-0003-4272-6558Abdullahi Abdu Ibrahim1Sinan Q. Salih2https://orcid.org/0000-0003-0717-7506Hussam S. Alhadawi3https://orcid.org/0000-0003-2179-4383Poh Soon JosephNg4https://orcid.org/0000-0002-6240-652XDepartment of Electrical and Computer Engineering, Altinbas University, Istanbul, TurkeyDepartment of Electrical and Computer Engineering, Altinbas University, Istanbul, TurkeyTechnical College of Engineering, Al-Bayan University, Baghdad, IraqDepartment of Computer Techniques Engineering, Dijlah University College, Baghdad, IraqFaculty of Data Science and Information Technology, INTI International University, Nilai, Negeri Sembilan, MalaysiaA metaheuristic approach based on the nature-inspired and well-known Grey Wolf Optimization algorithm (GWO) was employed in this study to design an approach for retrieving strong designs of <inline-formula> <tex-math notation="LaTeX">$8\times 8$ </tex-math></inline-formula> substitution boxes (S-boxes). The GWO was developed as a novel metaheuristic based on inspiration from grey wolves and how they hunt. The ability of the GWO to quickly explore the search space for the near/optimal feature subsets that maximize any given fitness function (in consideration of its distinctive hierarchical structure) aids in the construction of strong S-boxes that can satisfy the required criteria. However, when tackling optimization problems, GWO may experience the problem of premature convergence. Therefore, a variant of GWO called Crossover Grey Wolf Optimizer (XGWO) has been proposed in this study. The performance of the proposed novel approach was evaluated using numerous cryptographic performance metrics, including bijective property, bit independence, strict avalanche, linear probability, and I/O XOR distribution and the result was contrasted with a couple of existing S-box creation techniques. Overall, the results of the experiment showed that the suggested S-box design had adequate cryptographic features.https://ieeexplore.ieee.org/document/10103220/Substitution boxesoptimizationnature-inspired algorithmsGrey Wolf Optimizercryptology |
| spellingShingle | Ali Ibrahim Lawah Abdullahi Abdu Ibrahim Sinan Q. Salih Hussam S. Alhadawi Poh Soon JosephNg Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization IEEE Access Substitution boxes optimization nature-inspired algorithms Grey Wolf Optimizer cryptology |
| title | Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization |
| title_full | Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization |
| title_fullStr | Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization |
| title_full_unstemmed | Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization |
| title_short | Grey Wolf Optimizer and Discrete Chaotic Map for Substitution Boxes Design and Optimization |
| title_sort | grey wolf optimizer and discrete chaotic map for substitution boxes design and optimization |
| topic | Substitution boxes optimization nature-inspired algorithms Grey Wolf Optimizer cryptology |
| url | https://ieeexplore.ieee.org/document/10103220/ |
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