Preventive and Reactive Cybersecurity Techniques on IoT Devices in Healthcare Environments

In the rapidly evolving landscape of the Internet of Medical Things (IoMT), ensuring the security and reliability of interconnected medical devices is paramount. Traditional cybersecurity methods, while extensively discussed, often fall short in detecting and responding to evolving threats. Anomaly-based Intrusion Detection Systems (IDS) leveraging Artificial Intelligence (AI) techniques are increasingly favored for their ability to detect zero-day attacks and adapt to new threat landscapes. Accordingly, this doctoral thesis aims to develop and validate AI-based IDS for identifying and mitigating cybersecurity threats within IoMT environments. By utilizing publicly available datasets and generating a novel dataset from real-world IoMT devices and simulated cyberattacks, this study enhances detection system performance and investigates the generalizability of the proposed IDS across diverse environments using innovative AI techniques. Additionally, it explores and designs effective automated prevention techniques to enhance patient-centric cybersecurity against potential threats. The findings of this research are expected to significantly contribute to IoMT security, offering practical solutions for protecting critical healthcare infrastructure and ensuring patient safety.