Digital-Twin-Empowered Cybersecurity Framework for Healthcare Vulnerability Assessment
Abstract
This study presents a Digital Twin-enhanced cyber-security framework for real-time anomaly detection and dynamic vulnerability management in intelligent healthcare systems. The framework integrates a hybridBayesian with CNN-LSTMmodel within the DT environment to accurately identify abnormal patient vitals and device behaviors. To secure diagnostic data, a blockchain-based security layer with aReputation-Aware Fault-Tolerant Consensus (RAFTC)mechanism is employed, enabling immutable, decentralized, and tamper-resistant data sharing. Validation on healthcare data demonstrates superior performance, achieving a low latency of 9.25s, high precision (94.78%), sensitivity (96.89%), specificity (96.95%), and F-measure of 95.79%.
Fields of Study & Subjects
Smart Grid Security and Resilience
Anomaly Detection Techniques and Applications
Information and Cyber Security
Publication details
- Journal / source
- IEEE Internet of Things Journal
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 13
- Issue
- 11
- Pages
- 23471–23479
- Publication date
- 9 March 2026