Publication Type
Journal Article
Publication Date (Issue Year)
2025
Journal Name
IET Communications
Abstract
The increasing use of the Internet of Medical Things (IoMT) in healthcare highlights privacy and security concerns surroundingsensitive health data. This research focuses on enhancing the security and usability of IoMT for young users through arobust, adaptive continuous authentication model using physiological biometrics on Android devices and heart rate data fromsmartwatches. By integrating user behavior, environmental context, and health conditions, the model dynamically determinesrisk, trust, and authorization decisions. Machine learning techniques analyse data related to devices, networks, locations, anduser habits while considering demographics like age and medical conditions to assign suitable authenticators. The model balancesaccuracy and usability, favouring correct positive predictions, but faces limitations such as class imbalance, feature selection,and overfitting, with a false rejection rate (FRR) of 19%. Behavioral biometrics, personalized authentication, and continuousauthentication enhance security and accessibility. However, moderate sensitivity affects its ability to capture all positive cases.Age-group analysis reveals varying engagement with technology, emphasising tailored authentication flows. Future work willexplore explainable AI, context-aware analytics, and advanced risk assessments, integrating complementary smartwatch data likestep count for improved accuracy. This research demonstrates the potential of risk-based adaptive authentication to deliver secure,user-friendly solutions in complex healthcare environments
Keywords
Naïve Bayes Based, Android Adaptive, User Authentication, Prototype, Young Internet of Medical Things Users
Rsif Scholar Name
Prudence Munyaradzi Mavhemwa
Thematic Area
ICTs Including Big Data and Artificial Intelligence
Africa Host University (AHU)
University of Rwanda (UR), Rwanda
Funding Statement
This work was jointly supported by the African Centre of Excellence inInternet of Things (ACEIoT) from the College of Science and Technology,University of Rwanda, and the Regional Innovation Scholarship Fund(RSIF)
Recommended Citation
Mavhemwa, P. M., Zennaro, M., Nsengiyumva, P., & Nzanywayingoma, F. (2025). Naïve Bayes Based Android Adaptive User AuthenticationPrototype for Young Internet of Medical Things Users. IET Communications https://doi.org/doi.org/10.1049/cmu2.70082