Improving Security Performance of Healthcare Data in the Internet of Medical Things using a Hybrid Metaheuristic Model

Abstract Internet of medical things (IoMT) network design integrates multiple healthcare devices to improve patient monitoring and real-time care operations. These networks use a wide range of devices to make critical patient care decisions. Thus, researchers have deployed multiple high-security frameworks with encryption, hashing, privacy preservation, attribute based access control, and more to secure these devices and networks. However, real-time monitoring security models are either complex or unreconfigurable. The existing models’ security depends on their internal configuration, which is rarely extensible for new attacks. This paper introduces a hybrid metaheuristic model to improve healthcare IoT security performance. The blockchain based model can be dynamically reconfigured by changing its encryption and hashing standards. The proposed model then continuously optimizes blockchain based IoMT deployment security and QoS performance using elephant herding optimization (EHO) and grey wolf optimization (GWO). Dual fitness functions improve security and QoS for multiple attack types in the proposed model. These fitness functions help reconfigure encryption and hashing parameters to improve performance under different attack configurations. The hybrid integration of EH and GW optimization models can tune blockchain based deployment for dynamic attack scenarios, making it scalable and useful for real-time scenarios. The model is tested under masquerading, Sybil, man-in-the-middle, and DDoS attacks and is compared with state-of-the-art models. The proposed model has 8.3% faster attack detection and mitigation, 5.9% better throughput, a 6.5% higher packet delivery ratio, and 10.3% better network consistency under attack scenarios. This performance enables real-time healthcare use cases for the proposed model.