An Ontology-Driven AI Framework for Context-Aware Threat Detection in Mobile Health Applications
Loading...
Files
Size: 2.16 MB, File format: Adobe PDF
Date
Authors
Ferdous, Raiyan
Supervisor
Petrova, Krassie
Item type
Journal Title
Journal ISSN
Volume Title
Publisher
Auckland University of Technology
Abstract
Mobile health (mHealth) applications have substantially improved the accessibility of healthcare, enabling remote monitoring, chronic-disease management, and personalized interventions. However, because these applications collect and transmit sensitive personal health information on resource-constrained mobile devices, they introduce significant security and privacy risks, including weak authentication, unsafe data sharing, and a lack of awareness of user context (Luxton et al., 2012; Aljedaani & Babar, 2021). Traditional, signature-based security methods are largely static and frequently fail to adapt to the changing situations in which mobile users operate, leaving context-dependent threats poorly addressed.
This thesis proposes a context-aware mHealth security architecture and develops an OWL 2 DL contextual ontology in Protégé. It separately evaluates a candidate machine-learning (ML) detection component on a network-intrusion benchmark. The architectural integration is a design proposition; its effect on detection performance has not been tested.
The empirical work consists of a Python/scikit-learn classification pipeline applied to NSL-KDD network-connection records, following the publicly available Kaggle notebook by josiagiven (n.d.). The ontology is a design artefact. The Android context-collection layer, ontology-to-ML transformation, mitigation module, and on-device application were designed but not implemented or empirically evaluated. To preserve participant privacy and satisfy ethical requirements, no real patient data were used.
The appendix records point estimates from a validation split, including Decision Tree accuracy of 0.983, KNN accuracy of 0.995, and Linear SVC accuracy of 0.981; the displayed Random Forest confusion matrix contains no errors and reports AUC = 1.00. These unusually high scores require verification for leakage and proxy features before being interpreted as reliable out-of-sample performance. The different ranges in Table 4.4 are retained as originally reported but cannot be identified as estimates from that same run without the fold-level records.
Latency below 150 ms and CPU utilization below 12% were proposed design targets. No Android deployment or device resource measurement was carried out.
The contribution is a specified ontology and mHealth security architecture together with an offline benchmark evaluation of a candidate ML detection layer. Whether semantic enrichment improves detection or an on-device implementation is feasible remains open to empirical testing.