Abstract:
Introducing IoT systems into healthcare applications has revolutionized patient monitoring, enabling remote access to vital information and timely diagnostics. However, ensuring the security and confidentiality of patient data remains a significant challenge, with unauthorized alterations posing risks to patient well-being, especially in emergency scenarios. Leveraging machine learning for intrusion detection in such systems shows promise due to the complexity of the data involved. Many existing healthcare intrusion detection systems focus solely on network flow metrics or patient biometrics. The project, known as “Intelligent TMS for IoMT”, presents an effective solution as it not only enhances patient safety by identifying and classifying intrusions effectively but also streamlines the workflow for cyber analysts by presenting vital patient data and security alerts in a unified interface, making their monitoring and decision-making processes more efficient.The system incorporates a user-friendly dashboard that presents patient vitals alongside intrusion attempts, providing cyber analysts with comprehensive insights into patient health. Additionally, we conducted an extensive comparison of various machine learning classifiers to assess their performance in intrusion detection, highlighting the strength and effectiveness of our system in identifying and classifying intrusions, thereby ensuring the safety of patients’ medical data and devices.
