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

Fraudulent 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 fraud...
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Autor*in:	Fillemon S. Enkono [verfasserIn] Nalina Suresh [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	m-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 selection

Übergeordnetes Werk:	In: The African Journal of Information and Communication - LINK Centre, School of Literature Language and Media (SLLM), 2019, (2020), 25, Seite 13
Übergeordnetes Werk:	year:2020 ; number:25 ; pages:13

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

Katalog-ID:	DOAJ015362779

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520			\|a Fraudulent 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.
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650		4	\|a short message service messages (smses)
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650		4	\|a fraud
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650		4	\|a detection
650		4	\|a machine learning
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650		4	\|a naïve bayes (nb)
650		4	\|a support vector machine (svm)
650		4	\|a classification accuracy (ca)
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allfields	(DE-627)DOAJ015362779 (DE-599)DOAJ85c6fbee5c674d67b4f92c40e78c3ca8 DE-627 ger DE-627 rakwb eng T58.5-58.64 Fillemon S. Enkono verfasserin aut Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fraudulent 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. m-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 selection Technology T Information technology Nalina Suresh verfasserin aut In The African Journal of Information and Communication LINK Centre, School of Literature Language and Media (SLLM), 2019 (2020), 25, Seite 13 (DE-627)76922038X (DE-600)2734620-1 20777213 nnns year:2020 number:25 pages:13 https://doi.org/10.23962/10539/29195 kostenfrei https://doaj.org/article/85c6fbee5c674d67b4f92c40e78c3ca8 kostenfrei https://hdl.handle.net/10539/29195 kostenfrei https://doaj.org/toc/2077-7205 Journal toc kostenfrei https://doaj.org/toc/2077-7213 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 2020 25 13
spelling	(DE-627)DOAJ015362779 (DE-599)DOAJ85c6fbee5c674d67b4f92c40e78c3ca8 DE-627 ger DE-627 rakwb eng T58.5-58.64 Fillemon S. Enkono verfasserin aut Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fraudulent 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. m-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 selection Technology T Information technology Nalina Suresh verfasserin aut In The African Journal of Information and Communication LINK Centre, School of Literature Language and Media (SLLM), 2019 (2020), 25, Seite 13 (DE-627)76922038X (DE-600)2734620-1 20777213 nnns year:2020 number:25 pages:13 https://doi.org/10.23962/10539/29195 kostenfrei https://doaj.org/article/85c6fbee5c674d67b4f92c40e78c3ca8 kostenfrei https://hdl.handle.net/10539/29195 kostenfrei https://doaj.org/toc/2077-7205 Journal toc kostenfrei https://doaj.org/toc/2077-7213 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 2020 25 13
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allfieldsGer	(DE-627)DOAJ015362779 (DE-599)DOAJ85c6fbee5c674d67b4f92c40e78c3ca8 DE-627 ger DE-627 rakwb eng T58.5-58.64 Fillemon S. Enkono verfasserin aut Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fraudulent 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. m-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 selection Technology T Information technology Nalina Suresh verfasserin aut In The African Journal of Information and Communication LINK Centre, School of Literature Language and Media (SLLM), 2019 (2020), 25, Seite 13 (DE-627)76922038X (DE-600)2734620-1 20777213 nnns year:2020 number:25 pages:13 https://doi.org/10.23962/10539/29195 kostenfrei https://doaj.org/article/85c6fbee5c674d67b4f92c40e78c3ca8 kostenfrei https://hdl.handle.net/10539/29195 kostenfrei https://doaj.org/toc/2077-7205 Journal toc kostenfrei https://doaj.org/toc/2077-7213 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 2020 25 13
allfieldsSound	(DE-627)DOAJ015362779 (DE-599)DOAJ85c6fbee5c674d67b4f92c40e78c3ca8 DE-627 ger DE-627 rakwb eng T58.5-58.64 Fillemon S. Enkono verfasserin aut Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Fraudulent 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. m-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 selection Technology T Information technology Nalina Suresh verfasserin aut In The African Journal of Information and Communication LINK Centre, School of Literature Language and Media (SLLM), 2019 (2020), 25, Seite 13 (DE-627)76922038X (DE-600)2734620-1 20777213 nnns year:2020 number:25 pages:13 https://doi.org/10.23962/10539/29195 kostenfrei https://doaj.org/article/85c6fbee5c674d67b4f92c40e78c3ca8 kostenfrei https://hdl.handle.net/10539/29195 kostenfrei https://doaj.org/toc/2077-7205 Journal toc kostenfrei https://doaj.org/toc/2077-7213 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 2020 25 13
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topic_facet	m-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 selection Technology T Information technology
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author	Fillemon S. Enkono
spellingShingle	Fillemon S. Enkono misc T58.5-58.64 misc m-banking misc e-wallets misc short message service messages (smses) misc deposit notification misc fraud misc ham smses misc scam smses misc detection misc machine learning misc classifiers misc naïve bayes (nb) misc support vector machine (svm) misc classification accuracy (ca) misc feature extraction misc feature selection misc Technology misc T misc Information technology Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes
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topic_title	T58.5-58.64 Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes m-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 selection
topic	misc T58.5-58.64 misc m-banking misc e-wallets misc short message service messages (smses) misc deposit notification misc fraud misc ham smses misc scam smses misc detection misc machine learning misc classifiers misc naïve bayes (nb) misc support vector machine (svm) misc classification accuracy (ca) misc feature extraction misc feature selection misc Technology misc T misc Information technology
topic_unstemmed	misc T58.5-58.64 misc m-banking misc e-wallets misc short message service messages (smses) misc deposit notification misc fraud misc ham smses misc scam smses misc detection misc machine learning misc classifiers misc naïve bayes (nb) misc support vector machine (svm) misc classification accuracy (ca) misc feature extraction misc feature selection misc Technology misc T misc Information technology
topic_browse	misc T58.5-58.64 misc m-banking misc e-wallets misc short message service messages (smses) misc deposit notification misc fraud misc ham smses misc scam smses misc detection misc machine learning misc classifiers misc naïve bayes (nb) misc support vector machine (svm) misc classification accuracy (ca) misc feature extraction misc feature selection misc Technology misc T misc Information technology
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title	Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes
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title_full	Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes
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title_sort	application of machine learning classification to detect fraudulent e‑wallet deposit notification smses
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title_auth	Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes
abstract	Fraudulent 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.
abstractGer	Fraudulent 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.
abstract_unstemmed	Fraudulent 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.
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title_short	Application of Machine Learning Classification to Detect Fraudulent E‑wallet Deposit Notification SMSes
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