Secure healthcare data sharing and attack detection framework using radial basis neural network

Secure medical data sharing and access control play a prominent role. However, it is still unclear how to provide a security architecture that can guarantee the privacy and safety of sensitive medical data. Existing methods are application-specific and fail to take into account the complex security needs of healthcare applications. Moreover, the healthcare sector needs dynamic permission enforcement, extensible context-aware access control, flexible, and on-demand authentication. Therefore, this research proposes an access control mechanism and an effective attack detection model. The proposed authenticate access control mechanism (PA2C) safeguards data integrity as well as the security and dependability of EHR data sharing are improved by the use of smart contracts, encryption, and secure key management. On the other hand, the proposed intelligent voyage optimization algorithm-based Radial basis neural network (IntVO-RBNN) effectively detects the attacks in the network. Specifically, the Intelligent Voyage Optimization algorithm effectively tunes the model hyperparameters and the deployment of hybrid features contributes to the proposed model to detect attack patterns effectively. The comparative results showed that the suggested access control strategy performed better than the current methods in terms of minimal responsiveness of 100.18 s and less information loss of 4.49% for 100 blocks. Likewise, the proposed IntVO-RBNN attack detection model performs better with 95.26% recall, 97.84% precision, and 94.02% accuracy.