A Novel GWO-Optimized Chaotic Map for Medical Image Encryption in IoT Healthcare

Abstract

Strong encryption methods are necessary to protect medical images against illegal access and cyber threats as digital healthcare systems and telemedicine expand exponentially. This work presents a Grey Wolf Optimizer (GWO) enhanced encryption system using a modified logistic chaotic map to guarantee excellent security and efficiency in medical image encryption. The suggested method optimizes chaotic parameters against increased Shannon entropy and decreased pixel correlation, guaranteeing enhanced unpredictability and resilience against statistical attacks. Key generation from pixel intensities, chaotic sequence generation, and XOR-based encryption form the encryption process. Driven by GWO, the optimization process reduces chaotic parameters to generate an encrypted image with low correlation, almost uniform histogram, and high entropy. The suggested method is fit for real-time IoT-based medical applications based on experimental results on standard medical pictures, including MRI-chest and standard image Lena, showing the robustness of the proposed method in terms of NPCR, UACI, PSNR, and entropy analysis. While preserving computational efficiency, the suggested encryption