Novel knowledge graph-based modeling for vulnerability detection in the Internet of Medical Things

Authors

Kulsoom Saima Bughio, Edith Cowan UniversityFollow
David Michael Cook, Edith Cowan UniversityFollow
Syed Afaq Ali Shah, Edith Cowan UniversityFollow

Document Type

Conference Proceeding

Publication Title

Communications in Computer and Information Science

First Page

314

Last Page

325

Publisher

Springer

School

School of Science

Comments

Bughio, K. S., Cook, D. M., & Shah, S. A. A. (2024, April). Novel Knowledge Graph-Based Modeling for Vulnerability Detection in the Internet of Medical Things. In Asian Conference on Intelligent Information and Database Systems (pp. 314-325). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5937-8_26

Abstract

In the evolving landscape of the Internet of Medical Things (IoMT) cybersecurity, traditional security measures often struggle with complex vulnerabilities, which are crucial due to the sensitive nature of patients’ data. This article addresses this challenge and presents a semantic framework to enhance cybersecurity on IoMT. It proposes a novel MIoT (Medical Internet of Things) ontology that integrates knowledge from diverse sources and employs RDF (Resource Description Framework) formalism for the semantic representation of medical devices and their related aspects. The framework also utilizes semantic modelling to enrich data annotation and knowledge base development, supporting the detection of vulnerabilities in medical IoT (Internet of Things) networks. Additionally, the framework generates a knowledge graph that stores Cyberthreat Intelligence (CTI) for medical IoT networks, enhancing vulnerability detection, while underscoring the significance of automated reasoning over aggregated knowledge.

DOI

10.1007/978-981-97-5937-8_26

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