Clone Attack Detection Based on Novel Lightweight Security Approach Using Bio-Medical Data Protection Scheme for Remote Healthcare Management

Wearable sensors in a healthcare system enabled by the Internet of Things may collect biomedical healthcare data remotely and transmit it to healthcare practitioners through open wireless channels. An attacker may seize nodes, recompile the software, and rapidly replicate it to gain network control. Security is a major issue under these conditions. Real-time health monitoring afforded by a wireless IoT-Sensor network necessitates careful design of network attack detection and recovery mechanisms. For this reason, this study presents a novel lightweight security approach for detecting and recovering clone nodes (LSP-DRCN) in a wireless health monitoring network. Using a three-stage fuzzy-based clone detection technique (TSF-CDS) with clone node disconnection, the suggested model can identify clone attacks as soon as they are initiated. A critical energy analysis model has been employed to ensure that the proposed LSP-DRCN would be energy efficient. A simulation study has been employed to evaluate the suggested model; it outperformed previous models regarding clone node detection rate, network longevity, and energy optimization ratio while using less energy overall.