Naïve Bayes Based Android Adaptive User Authentication Prototype for Young Internet of Medical Things Users

Abstract

The increasing use of the Internet of Medical Things (IoMT) in healthcare highlights privacy and security concerns surrounding sensitive health data. This research focuses on enhancing the security and usability of IoMT for young users through a robust, adaptive continuous authentication model using physiological biometrics on Android devices and heart rate data from smartwatches. By integrating user behavior, environmental context, and health conditions, the model dynamically determines risk, trust, and authorization decisions. Machine learning techniques analyse data related to devices, networks, locations, and user habits while considering demographics like age and medical conditions to assign suitable authenticators. The model balances accuracy 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 continuous authentication 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 will explore explainable AI, context-aware analytics, and advanced risk assessments, integrating complementary smartwatch data like step count for improved accuracy. This research demonstrates the potential of risk-based adaptive authentication to deliver secure, user-friendly solutions in complex healthcare environments.