An Enhanced Chaotic Non-linear S-Box Design for Securing IoT-Based Healthcare Environment

Abstract

This paper presents a hybrid approach for designing a highly secure Substitution Box (S-Box) for protecting sensitive patient data in an IoT-based healthcare environment. Leveraging a combination of evolutionary heuristics, deep learning algorithms, and chaotic systems, the design aims to offer improved cryptographic security. The proposed system enhances nonlinearity through chaotic maps and dynamically evolves using deep learning models to resist differential and linear cryptanalysis attacks. This novel approach is designed to operate efficiently within resource-constrained IoT systems while maintaining high performance for real-time data protection.