Ahmad Musheer, Al-Solami Eesa
Department of Computer Engineering, Jamia Millia Islamia, New Delhi 110025, India.
Department of Information Security, University of Jeddah, Jeddah 21493, Saudi Arabia.
Entropy (Basel). 2020 Jun 28;22(7):717. doi: 10.3390/e22070717.
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.
固定结构分组密码中的静态替换盒可能会使系统易受密码分析的攻击。然而，依赖密钥的动态替换盒（S盒）被认为可以提高整个密码系统的安全性和鲁棒性。本文提出构建具有高非线性的依赖密钥的动态S盒。所提出的方案涉及基于分数阶时延霍普菲尔德神经网络对初始生成的S盒进行演化，以提高其非线性。通过使用包括非线性、严格雪崩准则、比特独立性准则、差分均匀性、线性逼近概率等标准安全参数来评估演化后的S盒的加密性能。所提出的方案能够演化出一个平均非线性为111.25、严格雪崩准则值为0.5007且差分均匀性为10的S盒。性能评估表明，所提出的方案和S盒具有优异的特性，因此能够在密码系统中提供高非线性。对比分析进一步证实了与许多现有的基于混沌的S盒及其他S盒相比，预期方案和S盒具有更高的安全特性。
