Proactive Cyber-Threat Intelligence and Predictive Analytics for Protecting At-Home Medical IoT Devices against Zero-Day Exploits

Abstract

This research developed a predictive and cybersecurity-aware framework to uncover and leverage underreported clinical and operational signals within dark data embedded in digital health ecosystems. Addressing the paradox of data-rich yet insight-poor healthcare systems, the study adopted a sequential explanatory mixed-methods design that combined quantitative machine learning analysis with qualitative stakeholder evaluation. The datasets incorporated sources such as CIC-IDS-2018, IoT-23, and the Zero-Day Exploit Corpus, reflecting medical IoT environments like smartwatches and insulin pumps connected through Wi-Fi, Bluetooth, and 5G networks. Neural network models achieved an overall threat and anomaly detection rate of 95.9%, with cardiac monitor data performing best at 97.1% due to distinctive behavioral patterns. The framework identified novel clinical and cyber-physical signals, improving rare disease detection and reducing false positives, thereby enhancing reliability and trust. Qualitative feedback from healthcare practitioners confirmed the system’s usability and interpretability. The integration of adversarial simulation data strengthened resilience against zero-day threats, positioning the framework as a scalable solution for improving patient safety, regulatory compliance, and precision medicine in digital healthcare.