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Open AccessArticle

Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme

1
Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India
2
Department of Information Security, University of Jeddah, Jeddah 21493, Saudi Arabia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(7), 717; https://doi.org/10.3390/e22070717
Received: 30 May 2020 / Revised: 16 June 2020 / Accepted: 27 June 2020 / Published: 28 June 2020
(This article belongs to the Section Information Theory, Probability and Statistics)
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. View Full-Text
Keywords: dynamic S-box; block cryptosystem; fractional Hopfield neural network; security dynamic S-box; block cryptosystem; fractional Hopfield neural network; security
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MDPI and ACS Style

Ahmad, M.; Al-Solami, E. Evolving Dynamic S-Boxes Using Fractional-Order Hopfield Neural Network Based Scheme. Entropy 2020, 22, 717.

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