Enterprise AI Analysis

Intelligent decision-making framework for selecting optimized substitution boxes in modern block cipher design

The nonlinear confusion component of block ciphers plays a crucial role in data secrecy, and choosing the best substitution box (S-box) is a difficult task since there are various competing cryptographic requirements. These S-boxes have various cryptographic strengths, and thus their comparison constitutes a multi-criteria optimization task. This paper presents a hybrid Multi-Criteria Decision-Making (MCDM) approach that combines the Evaluation based on Distance from Average Solution (EDAS) technique and Entropy-weighting framework to systematically rank S-boxes. The new model presents a clear, data-oriented evaluation of primary cryptographic parameters such as nonlinearity, strict avalanche criterion, bit independence, differential approximation probability and linear approximation probability. The results emphasize that the S-boxes that have higher linear and differential attack resistance to provide designers with evidence-oriented design directions towards building secure and efficient cipher schemes. The future developments will extend this approach to AI-generated and optimization-algorithm driven S-box designs, furthering research towards efficient cryptographic schemes for digital confidentiality.