Safeguarding Digital Health
Raghunath Maji, Biswajit Gayen · 2025
The proliferation of smart healthcare systems has introduced a new era of patient care, leveraging interconnected medical devices to improve monitoring and treatment. However, this enhanced connectivity also exposes healthcare environments to potential cyber security threats, including malicious devices. Such devices can compromise patient safety, data integrity, and system reliability. To tackle this pressing concern, we present a pioneering approach aimed at detecting and identifying malicious devices within a smart healthcare system by analyzing their behavioral activities. This innovative methodology strives to fortify the system's security measures, ensuring a proactive stance against potential threats and bolstering the overall integrity of the healthcare infrastructure. Our proposed system employs advanced machine learning algorithms, anomaly detection techniques, and behavioral profiling to continuously monitor and analyze the behavior of interconnected devices. By establishing a baseline of normal device behavior, our system can identify deviations indicative of potential malicious intent. Key behavioral parameters, such as data transmission patterns, access frequency, and communication protocols, are scrutinized in real-time to detect suspicious activities. The main aim of our study offer healthcare providers and system administrators a powerful tool to secure their smart healthcare ecosystems against potential threats. By swiftly detecting and isolating malicious devices, we can safeguard patient safety and data confidentiality, instilling confidence among users and enhancing overall trust in smart healthcare technologies.