Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes

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dc.article.end-page13en_ZA
dc.article.start-page1en_ZA
dc.citation.doihttps://doi.org/10.23962/10539/29195en_ZA
dc.contributor.authorEnkono, Fillemon S.
dc.contributor.authorSuresh, Nalina
dc.date.accessioned2020-06-30T15:21:59Z
dc.date.available2020-06-30T15:21:59Z
dc.date.issued2020-06-30
dc.description.abstractFraudulent e-wallet deposit notification SMSes designed to steal money and goods from m-banking users have become pervasive in Namibia. Motivated by an observed lack of mobile applications to protect users from such deceptions, this study evaluated the ability of machine learning to detect the fraudulent e-wallet deposit notification SMSes. The naïve Bayes (NB) and support vector machine (SVM) classifiers were trained to classify both ham (desired) SMSes and scam (fraudulent) e-wallet deposit notification SMSes. The performances of the two classifier models were then evaluated. The results revealed that the SVM classifier model could detect the fraudulent SMSes more efficiently than the NB classifier.en_ZA
dc.description.librarianCA2020en_ZA
dc.facultyHumanitiesen_ZA
dc.identifier.citationEnkono, F. S., & Suresh, N. (2020). Application of machine learning classification to detect fraudulent e‑wallet deposit notification SMSes. The African Journal of Information and Communication (AJIC), 25, 1-13. https://doi.org/10.23962/10539/29195en_ZA
dc.identifier.issn2077-7213 (online version)
dc.identifier.issn2077-7205 (print version)
dc.identifier.urihttps://hdl.handle.net/10539/29195
dc.journal.issue25en_ZA
dc.journal.linkhttps://www.wits.ac.za/linkcentre/ajicen_ZA
dc.journal.titleThe African Journal of Information and Communication (AJIC)en_ZA
dc.language.isoenen_ZA
dc.orcid.idEnkono: https://orcid.org/0000-0002-0891-4654en_ZA
dc.orcid.idSuresh: https://orcid.org/0000-0002-9846-7199en_ZA
dc.publisherLINK Centre, University of the Witwatersrand (Wits), Johannesburgen_ZA
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence: https://creativecommons.org/licenses/by/4.0en_ZA
dc.schoolSchool of Literature, Language and Media (SLLM)en_ZA
dc.subjectm-banking, e-wallets, short message service messages (SMSes), deposit notification, fraud, ham SMSes, scam SMSes, detection, machine learning, classifiers, naïve Bayes (NB), support vector machine (SVM), classification accuracy (CA), feature extraction, feature selectionen_ZA
dc.titleApplication of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSesen_ZA
dc.typeArticleen_ZA
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AJIC Issue 25, 2020