Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme
Static substitution-boxes in fixed structured block ciphers may make the system vulnerable to cryptanalysis. However, key-dependent dynamic substitution-boxes (S-boxes) assume to improve the security and robustness of the whole cryptosystem. This paper proposes to present the construction of key-dep...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-06-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/7/717 |
| _version_ | 1797563864084119552 |
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| author | Musheer Ahmad Eesa Al-Solami |
| author_facet | Musheer Ahmad Eesa Al-Solami |
| author_sort | Musheer Ahmad |
| collection | DOAJ |
| description | Static substitution-boxes in fixed structured block ciphers may make the system vulnerable to cryptanalysis. However, key-dependent dynamic substitution-boxes (S-boxes) assume to improve the security and robustness of the whole cryptosystem. This paper proposes to present the construction of key-dependent dynamic S-boxes having high nonlinearity. The proposed scheme involves the evolution of initially generated S-box for improved nonlinearity based on the fractional-order time-delayed Hopfield neural network. The cryptographic performance of the evolved S-box is assessed by using standard security parameters, including nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, etc. The proposed scheme is able to evolve an S-box having mean nonlinearity of 111.25, strict avalanche criteria value of 0.5007, and differential uniformity of 10. The performance assessments demonstrate that the proposed scheme and S-box have excellent features, and are thus capable of offering high nonlinearity in the cryptosystem. The comparison analysis further confirms the improved security features of anticipated scheme and S-box, as compared to many existing chaos-based and other S-boxes. |
| first_indexed | 2024-03-10T18:49:13Z |
| format | Article |
| id | doaj.art-3ba09aa1af4f4e26b4bf322327dd9065 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:49:13Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-3ba09aa1af4f4e26b4bf322327dd90652023-11-20T05:14:16ZengMDPI AGEntropy1099-43002020-06-0122771710.3390/e22070717Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based SchemeMusheer Ahmad0Eesa Al-Solami1Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, IndiaDepartment of Information Security, University of Jeddah, Jeddah 21493, Saudi ArabiaStatic substitution-boxes in fixed structured block ciphers may make the system vulnerable to cryptanalysis. However, key-dependent dynamic substitution-boxes (S-boxes) assume to improve the security and robustness of the whole cryptosystem. This paper proposes to present the construction of key-dependent dynamic S-boxes having high nonlinearity. The proposed scheme involves the evolution of initially generated S-box for improved nonlinearity based on the fractional-order time-delayed Hopfield neural network. The cryptographic performance of the evolved S-box is assessed by using standard security parameters, including nonlinearity, strict avalanche criterion, bits independence criterion, differential uniformity, linear approximation probability, etc. The proposed scheme is able to evolve an S-box having mean nonlinearity of 111.25, strict avalanche criteria value of 0.5007, and differential uniformity of 10. The performance assessments demonstrate that the proposed scheme and S-box have excellent features, and are thus capable of offering high nonlinearity in the cryptosystem. The comparison analysis further confirms the improved security features of anticipated scheme and S-box, as compared to many existing chaos-based and other S-boxes.https://www.mdpi.com/1099-4300/22/7/717dynamic S-boxblock cryptosystemfractional Hopfield neural networksecurity |
| spellingShingle | Musheer Ahmad Eesa Al-Solami Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme Entropy dynamic S-box block cryptosystem fractional Hopfield neural network security |
| title | Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme |
| title_full | Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme |
| title_fullStr | Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme |
| title_full_unstemmed | Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme |
| title_short | Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme |
| title_sort | evolving dynamic s boxes using fractional order hopfield neural network based scheme |
| topic | dynamic S-box block cryptosystem fractional Hopfield neural network security |
| url | https://www.mdpi.com/1099-4300/22/7/717 |
| work_keys_str_mv | AT musheerahmad evolvingdynamicsboxesusingfractionalorderhopfieldneuralnetworkbasedscheme AT eesaalsolami evolvingdynamicsboxesusingfractionalorderhopfieldneuralnetworkbasedscheme |