{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T14:14:31Z","timestamp":1768918471755,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T00:00:00Z","timestamp":1646352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Providing a dynamic access control model that uses real-time features to make access decisions for IoT applications is one of the research gaps that many researchers are trying to tackle. This is because existing access control models are built using static and predefined policies that always give the same result in different situations and cannot adapt to changing and unpredicted situations. One of the dynamic models that utilize real-time and contextual features to make access decisions is the risk-based access control model. This model performs a risk analysis on each access request to permit or deny access dynamically based on the estimated risk value. However, the major issue associated with building this model is providing a dynamic, reliable, and accurate risk estimation technique, especially when there is no available dataset to describe risk likelihood and impact. Therefore, this paper proposes a Neuro-Fuzzy System (NFS) model to estimate the security risk value associated with each access request. The proposed NFS model was trained using three learning algorithms: Levenberg\u2013Marquardt (LM), Conjugate Gradient with Fletcher\u2013Reeves (CGF), and Scaled Conjugate Gradient (SCG). The results demonstrated that the LM algorithm is the optimal learning algorithm to implement the NFS model for risk estimation. The results also demonstrated that the proposed NFS model provides a short and efficient processing time, which can provide timeliness risk estimation technique for various IoT applications. The proposed NFS model was evaluated against access control scenarios of a children\u2019s hospital, and the results demonstrated that the proposed model can be applied to provide dynamic and contextual-aware access decisions based on real-time features.<\/jats:p>","DOI":"10.3390\/s22052005","type":"journal-article","created":{"date-parts":[[2022,3,6]],"date-time":"2022-03-06T20:40:02Z","timestamp":1646599202000},"page":"2005","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Efficient NFS Model for Risk Estimation in a Risk-Based Access Control Model"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4142-6377","authenticated-orcid":false,"given":"Hany F.","family":"Atlam","sequence":"first","affiliation":[{"name":"School of Computing and Engineering, University of Derby, Derby DE22 1GB, UK"},{"name":"Computer Science Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf 32952, Egypt"}]},{"given":"Muhammad Ajmal","family":"Azad","sequence":"additional","affiliation":[{"name":"School of Computing and Engineering, University of Derby, Derby DE22 1GB, UK"}]},{"given":"Nawfal F.","family":"Fadhel","sequence":"additional","affiliation":[{"name":"Electronic and Computer Science Department, University of Southampton, Southampton SO17 1BJ, UK"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1007\/978-3-319-48057-2_20","article-title":"Trust and risk-based access control for privacy-preserving threat detection systems","volume":"Volume 10018","author":"Metoui","year":"2016","journal-title":"Lecture Notes in Computer Science"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, Q., and Jin, H. 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