AegisSentinel: Secure Medical Image Transmission with HFQE-S Encryption and COA Optimized HAPCNN Attack Detection

Abstract

Medical images such as computed tomography (CT), magnetic resonance imaging (MRI), and X-ray scans are commonly sent through health care networks for remote medical diagnostics and consultations. While sending, the said images become susceptible to being exposed to noise, tampered, and attacked, which might not seem visually perceptible yet could cause significant medical errors. In this study, we present AegisSentinel, an end-to-end solution that uses Hybrid Fibonacci Q-Matrix Encryption with SHA Key (HFQE-S) to secure medical images, a Hierarchical Auto-Associative Polynomial CNN (HAPCNN) to detect transmission attacks, and Crayfish Optimization Algorithm (COA) to tune the latter model. The algorithm was tested using 8,550 labeled CT scan images from the TCGA-LUAD dataset across six classes (clean and five types of attacks). The proposed encryption scheme yielded an entropy value of 7.9973 bits owing to a lossless decryption process with infinite PSNR. COA-tuned HAPCNN yielded 96.52% accuracy with 96.49% F1 score in classification while performing encryption and decryption within 2.87 ms and 2.35 ms, respectively.