Digital Twins for Patient Monitoring: A Cybersecurity Framework for Attack-Resilent Virtual Health Model

• Author(s): Ahmad Ikram
• Paper ID: 1709405
• Page: 1535-1545
• Published Date: 29-06-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 12 June-2025

Abstract

The integration of digital twins into patient monitoring systems constitutes the paradigm shift in healthcare delivery. These systems provide a dynamic, rich-data virtual replica of the patient to offer real-time simulation of clinical scenarios, prevention of an untoward event, and optimization of treatments. Nonetheless, such implementations come with great cybersecurity problems arising from real-time data exchange with AI inference engines and the reliance on IoMT devices. This paper proposes a full multilevel cybersecurity framework specially designed for the digital twin architecture in high-acuity settings. The framework synergizes Zero Trust identity management, blockchain data integrity, AI-based anomaly detection, end-to-end encryption, and automated compliance auditing under GDPR and HIPAA provisions. For testing purposes, a simulated testbed was developed, mimicking ICU-level operations and testing the system under the following five scenarios: data injection, adversarial AI input, insider threats, brute-force login attempts, and denial of service. Results indicate detection rates above 85% with very low false-positive rates of about 3 to 6%, very low latency overhead of less than 120 ms, and very high resilience scores of equal or greater than 0.88, thereby attesting to the reliability and viability of the architecture. This paper, in essence, makes up for that vital gap in cybersecurity intervention for digital health twins by offering a scalable, modular, and regulation-minded model ready for active deployment in a clinical setting. Governance-related strategic implications, clinician trust, and integration with existing hospital infrastructure are also discussed, with future objectives taking into account federated learning, explainable AI, and quantum-resilient encryption.

Citations

IRE Journals:
Ahmad Ikram "Digital Twins for Patient Monitoring: A Cybersecurity Framework for Attack-Resilent Virtual Health Model" Iconic Research And Engineering Journals Volume 8 Issue 12 2025 Page 1535-1545

IEEE:
Ahmad Ikram "Digital Twins for Patient Monitoring: A Cybersecurity Framework for Attack-Resilent Virtual Health Model" Iconic Research And Engineering Journals, 8(12)