📄 Sciences Methods and Technologies
International Journal (SciMeTech)

Volume 2 · Issue 1 · 2026
ISSN: 3085-5284
A Multidimensional Classification Model for Security Vulnerabilities in Medical Wireless Sensor Networks
NMARI FERDAOUSS, IDRISSI NAJLAE
Pages 182–192 · Intelligence, Data and Computing Team, Sultan Moulay Slimane University, Morocco
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Abstract
Medical wireless sensor networks (MWSNs) and wireless body area networks (WBANs) are becoming a prerequisite in today's healthcare. They enable high quality, continuous monitoring of patients, real-time collection of vital body signs, and remote diagnosis. Critical security issues in resource-limited medical sensor networks come from both their low energy and computational capabilities and their deployment in sensitive, clinical environments. Generally, analyses of vulnerabilities in sensor networks to attacks in the target incident tend to be limited to specific threats from a single perspective. This paper presents a unified framework containing a multidimensional, classification model for the analysis of specific security vulnerabilities to wireless sensor networks in medical applications. The model operates on three complementary dimensions: network layer, targeted resource, and security impact. A property of the model is support for multi-impact representation of attacks affecting more than one security property. We define the model formally as V = (L,R,I) and we validate it by mapping all thirty-two attacks selected from multiple, independent, existing taxonomies for wireless sensor networks onto our classification across the space of all communication layers from physical to application boundaries within healthcare applications. Three concrete use cases illustrate the applicability of the model to IDS rule generation, countermeasure prioritization, and coverage gap analysis based on the CICIoMT2024 benchmark dataset. An experimental validation with OMNeT++/Castalia confirms that the V = (L, R, I) vectors accurately predict the observable impacts of attacks on network performance. The findings establish the completeness, discriminability, and applicability of the proposed framework, highlighting that the network layer is disproportionately targeted and that availability and integrity are the most impacted security properties. The model offers a systematic basis for designing context-aware security mechanisms for next-generation Internet of Medical Things (IoMT) systems.
Keywords: Medical WSN, Security Vulnerabilities, Classification Model, IoMT Security, WBAN

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SciMeTech · Volume 2, Issue 1 · 2026 · ISSN 3085-5284