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dc.contributor.authorRahman, Md Arafatur
dc.contributor.authorZaman, Nafees
dc.contributor.authorAsyhari, A Taufiq
dc.contributor.authorSadat, SM Nazmus
dc.contributor.authorPillai, Prashant
dc.contributor.authorArshah, Ruzaini Abdullah
dc.date.accessioned2021-06-30T15:00:26Z
dc.date.available2021-06-30T15:00:26Z
dc.date.issued2021-07-09
dc.identifier.citationRahman, M.A., Zaman, N., Asyhari, A.T. et al. (2021) SPY-BOT: machine learning-enabled post filtering for social network-integrated industrial internet of things. Ad Hoc Networks, 121, Article number 102588en
dc.identifier.issn1570-8705en
dc.identifier.doi10.1016/j.adhoc.2021.102588
dc.identifier.urihttp://hdl.handle.net/2436/624163
dc.descriptionThis is an accepted manuscript of an article published by Elsevier in Ad Hoc Networks on 09/07/2021, available online: https://doi.org/10.1016/j.adhoc.2021.102588 The accepted version of the publication may differ from the final published version.en
dc.description.abstractA far-reaching expansion of advanced information technology enables ease and seamless communications over online social networks, which have been a de facto premium correspondents in the current cyber world. The ever-growing social network data has gained attention in recent years and can be handy for industrial revolution 4.0. With the integration of social networks with the Internet of Things being noticed in different industries to enhance human involvement and increase their productivity, security in such networks is increasingly alarming. Vulnerabilities can be characterized in the form of privacy invasion, leading to hazardous contents, which can be detrimental to social media actors and in turn impact the processes of the overall Social Network-Integrated Industrial Internet of Things (SN-IIoT) ecosystem. Despite this prevalence, the current platforms do not have any significant level of functionality to capture, process, and reveal unhealthy content among the social media actors. To address those challenges by detecting hazardous contents and create a stable social internet environment within IIoT, a statistical learning-enabled trustworthy analytic tool for human behaviors has been developed in this paper. More specifically, this paper proposes a machine learning (ML)-enabled scheme SPY-BOT, which incorporates a hybrid data extraction algorithm to perform post-filtering that arbitrates the users’ behavior polarity. The scheme creates class labels based on the featured keywords from the decision user and classifies suspicious contacts through the aid of ML. The results suggest the potential of the proposed approach to classify the users’ behavior in SN-IIoT.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S1570870521001256en
dc.subjectIIoTen
dc.subjectPost-Filteringen
dc.subjectbehavioral analysisen
dc.subjectcomputational methoden
dc.subjectmachine learningen
dc.subjectsocial networken
dc.subjectpost-filteringen
dc.subjectnatural language processingen
dc.titleSPY-BOT: machine learning-enabled post filtering for social network-integrated industrial internet of thingsen
dc.typeJournal articleen
dc.identifier.journalAd Hoc Networksen
dc.date.updated2021-06-30T11:12:59Z
dc.identifier.articlenumber102588
dc.date.accepted2021-06-10
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW30062021ARen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
rioxxterms.licenseref.startdate2022-07-09en
dc.source.volume121
refterms.dateFCD2021-06-30T14:59:49Z
refterms.versionFCDAM


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