The intensification of the Internet of Health Things devices created security concerns due to the limitations of these devices and the nature of the healthcare data. While dealing with the security challenges, several authentication schemes, protocols, processes, and standards have been adopted. Consequently, making the right decision regarding the installation of a secure authentication solution or procedure becomes tricky and challenging due to the large number of security protocols, complexity, and lack of understanding. The major objective of this study is to propose an IoHT-based assessment framework for evaluating and prioritizing authentication schemes in the healthcare domain. Initially, in the proposed work, the security issues related to authentication are collected from the literature and consulting experts’ groups. In the second step, features of various authentication schemes are collected under the supervision of an Internet of Things security expert using the Delphi approach. The collected features are used to design suitable criteria for assessment and then Graph Theory and Matrix approach applies for the evaluation of authentication alternatives. Finally, the proposed framework is tested and validated to ensure the results are consistent and accurate by using other multi-criteria decision-making methods. The framework produces promising results such as 93%, 94%, and 95% for precision, accuracy, and recall, respectively in comparison to the existing approaches in this area. The proposed framework can be picked as a guideline by healthcare security experts and stakeholders for the evaluation and decision-making related to authentication issues in IoHT systems
Modeling security evaluation framework for IoHT-driven systems using integrated decision-making methodology
Published 2024 in Scientific Reports
ABSTRACT
PUBLICATION RECORD
- Publication year
2024
- Venue
Scientific Reports
- Publication date
2024-05-28
- Fields of study
Medicine, Computer Science, Engineering
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
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