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This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>The proposed model\/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model\/algorithm.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.<\/jats:p><\/jats:sec>","DOI":"10.1108\/ics-04-2019-0046","type":"journal-article","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T09:03:41Z","timestamp":1592557421000},"page":"763-800","source":"Crossref","is-referenced-by-count":8,"title":["A novel trust model for fog computing using fuzzy neural networks and weighted weakest link"],"prefix":"10.1108","volume":"28","author":[{"given":"Mhamed","family":"Zineddine","sequence":"first","affiliation":[]}],"member":"140","reference":[{"key":"key2020110313135225700_ref001","article-title":"Common trust criteria for IaaS cloud evaluation and selection","volume-title":"International Conference on Computer and Information Sciences (ICCOINS)","year":"2014"},{"issue":"3","key":"key2020110313135225700_ref002","first-page":"425","article-title":"A fuzzy group decision-making model for multiple criteria based on Borda count","volume":"6","year":"2011","journal-title":"International Journal of the Physical Sciences"},{"key":"key2020110313135225700_ref003","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1145\/2307849.2307856","article-title":"Advancing the state of mobile cloud computing","volume-title":"Proceedings of the 3rd ACM Workshop on Mobile Cloud Computing and Services, MCS, 2012","year":"2012"},{"issue":"12","key":"key2020110313135225700_ref004","article-title":"Enabling dynamic data and indirect mutual trust for cloud computing storage systems","volume":"24","year":"2012","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"key2020110313135225700_ref005","first-page":"65","article-title":"Mobile edge computing: challenges for future virtual network embedding algorithms","volume-title":"Proceedings of the 8th International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP, 2014","year":"2014"},{"key":"key2020110313135225700_ref006","volume-title":"Concepts and Fuzzy Logic","year":"2011"},{"key":"key2020110313135225700_ref007","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/978-3-319-05029-4_7","article-title":"Fog computing: a platform for internet of things and analytics","volume-title":"Big Data and Internet of Things: A Roadmap for Smart Environments, in: Studies in Computational Intelligence","year":"2014"},{"key":"key2020110313135225700_ref008","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1145\/2342509.2342513","article-title":"Fog computing and its role in the internet of things","volume-title":"Proceedings of the 1st Edition of the MCC Workshop on Mobile Cloud Computing, 2012","year":"2012"},{"key":"key2020110313135225700_ref009","article-title":"Trust model for the private cloud","volume-title":"IEEE International Conference on Cyber Security, Cyber Warfare and Digital Forensic (CyberSec)","year":"2012"},{"key":"key2020110313135225700_ref010","article-title":"Common methodology for information technology security evaluation (CEM)","author":"CEM","year":"2012"},{"key":"key2020110313135225700_ref011","unstructured":"Certification Report (2017), MarkLogic Server 9. 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