Cybersecurity in Connected Medical Devices

Abstract

Connected medical devices -- insulin pumps with Bluetooth interfaces, implantable cardiac defibrillators with wireless telemetry, networked infusion pumps, cloud-connected imaging systems, and IoMT-enabled patient monitors -- have expanded the attack surface of healthcare delivery systems to encompass millions of wirelessly accessible endpoints whose compromise could directly endanger patient safety. The convergence of legacy device architectures designed without security considerations, resource-constrained embedded processors limiting cryptographic capability, and regulatory lifecycles spanning 10-20 years during which threat landscapes evolve dramatically creates a cybersecurity challenge unique to medical devices. This study presents the Connected Medical Device Cybersecurity Assessment Framework (CMDCAF), evaluating five cybersecurity strategies -- network segmentation and perimeter defence, device-level firmware hardening, cryptographic authentication and encryption, AI-based anomaly detection for network traffic, and zero-trust architecture with continuous device attestation -- across four security performance tasks: vulnerability reduction, attack detection capability, incident response time, and clinical workflow preservation during security events. Our Cybersecurity Effectiveness Score (CES) integrates vulnerability coverage, detection sensitivity, response timeliness, clinical continuity, and implementation feasibility. Zero-trust architecture achieved the highest CES (0.926) through continuous device identity verification and micro-segmented communication that contained 96% of simulated breaches to a single device without lateral movement, while AI-based anomaly detection achieved the highest attack detection sensitivity (0.958) through deep learning models trained on 2.8 million network flow records that identified novel attack patterns with 94% sensitivity and 4.2-minute mean detection latency.