Cryptographic Encryption and Optimization for Internet of Things Based Medical Image Security

The expansion of the Internet of Things is expected to lead to the emergence of the Internet of Medical Things (IoMT), which will revolutionize the health-care industry (IoT). The Internet of Things (IoT) revolution is outpacing current human services thanks to its bright mechanical, economical, and social future. Security is essential because most patient information is housed on a cloud platform in the hospital. The security of medical images in the Internet of Things was investigated in this research using a new cryptographic model and optimization approaches. For the effective storage and safe transfer of patient data along with medical images, a separate framework is required. The key management and optimization will be chosen utilizing the Rivest-Shamir-Adleman-based Arnold map (RSA-AM), hostile orchestration (HO), and obstruction bloom breeding optimization (OBBO) to increase the encryption and decryption processes' level of security. The effectiveness of the suggested strategy is measured using peak signal-to-noise ratio (PSNR), entropy, mean square error (MSE), bit error rate (BER), structural similarity index (SSI), and correlation coefficient (CC). The investigation shows that the recommended approach provides greater security than other current systems.