Abstract
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.
Highlights
With the recent advancements in the field of wired/wireless network communication and electronic sharing of confidential data, the need for information security within the organization has undergone major changes
S-boxes are responsible for creating confusion in data during the encryption process; due to this property of the S-box, it is the essential component of numerous block ciphers
The attack procedure of cryptanalysis was given by Biham and Shamir; it is related with developing imbalance on the input/output dissemination to assault block ciphers and S-boxes [47]
Summary
With the recent advancements in the field of wired/wireless network communication and electronic sharing of confidential data, the need for information security within the organization has undergone major changes. S-boxes are responsible for creating confusion in data during the encryption process; due to this property of the S-box, it is the essential component of numerous block ciphers. Various S-box construction methods have been proposed based on different forms of dynamical models like discrete, continuous, fractional-order, time-delayed, etc. Quantization-based algorithms were used for the construction of S-boxes, using the integer-order continuous-time dynamical systems. There exist many proposals for S-box construction where the dynamics of integer-order continuous systems (having dimension three or more) are applied. No S-box method has been investigated based on the two-state continuous dynamical system which is of fractional-order and time-delayed as well. A new S-box evolution scheme is proposed, using the dynamics of the fractional-order time-delayed two-state Hopfield neural network system.
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