A Chaotic-Initiated Discrete Optimization Framework for Cryptographically Strong AES S-Box Generation

Advanced Encryption Standard (AES) security relies on the Substitution box (S-Box), which provides nonlinearity and confusion. Because this algebraic form is fixed, the cipher can be attacked with algebraic and structural cryptanalysis. To address this shortcoming, we proposed generating AES-compatible S-Boxes utilising chaotic Fisher-Yates initialization and metaheuristic optimization. A multi-objective fitness function, including the Average NonLinearity (AvgNL), Minimum NonLinearity (MinNL), and Differential Uniformity (DU), is used to select the optimal S-Box. The experimental results show that the constructed S-Box has a considerable average nonlinearity of about 112, and average Strict Avalanche Criterion (SAC) and Bit Independence Criterion (BIC) Non-linearity values of 0.50048 and 104.285, respectively. Also, its NPCR and UACI are approximately 99.5924 and 33.3214, respectively. Moreover, histogram, correlation, and entropy analyses of images encrypted by the proposed system indicate that the proposed S-Box provides stronger security and greater flexibility for image encryption.