Improvement of medical data security using SABES optimization algorithm

This paper proposes a bit-level cryptographic model for encrypting medical signals, patient information, clinical data, and images of various modalities. The model has utilized chaotic maps due to properties like non-periodicity, nonlinearity, ergodicity, deterministic, unpredictable, and sensitive to the initial conditions. Three chaotic maps are employed in a chained sequence at different levels of the model to elevate the randomness and provide uniform key distribution with larger key space. Also, the self-adaptive bald eagle search optimization algorithm has been employed to enhance the properties of chaotic maps by optimizing the initialization and control parameters. The cyclic redundancy check approach is involved to detect any change in the data during the transmission at the receiver side along with circular shift to make encrypted data resilient of differential attack. Therefore, the encryption model boosts the unpredictability, security, and protection against a wide range of threats. The encryption model performance efficiency is demonstrated using different types of security analysis to show resistance against statistical, entropy, differential, cropping, and brute-force attacks.