Linguistic neural networks for optimizing S-box selection in image encryption

S-boxes are essential in image encryption, particularly in block ciphers, but selecting the best one is challenging due to the complexity and uncertainty involved in evaluating multiple criteria. Traditional methods often struggle to handle this complexity, making the decision process unclear and unreliable. We evaluate 8 x 8 S-boxes by applying them to two images and analyzing their performance across multiple criteria. Since each criterion suggests a different S-box, the selection process becomes confusing, reflecting a real-world challenge in encryption security. To address this challenge, we propose an intelligent decision-making model based on linguistic neural networks to select the most suitable S-box for image encryption. The proposed model consistently identifies Skipjack as the most effective S-box for image encryption. To ensure its accuracy and reliability, we compare it with existing MCDM models using statistical analysis and performance ranking. The results consistently confirm Skipjack as the best choice, demonstrating that the proposed model enhances security, improves decision-making, and strengthens encryption efficiency.