Application of Machine Learning Classification to Detect Fraudulent E-wallet Deposit Notification SMSes
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1 janvier 2020
- handle: 10670/1.lz1cnt
Mots-clés
m-banking e-wallets, short message service messages (SMSes) deposit notification fraud ham SMSes scam SMSes detection machine learning classifiers naive Bayes (NB) support vector machine (SVM) classification accuracy (CA) feature extraction feature selectionSujets proches
Learning, MachineCiter ce document
Fillemon S. Enkono et al., « Application of Machine Learning Classification to Detect Fraudulent E-wallet Deposit Notification SMSes », African Journal of Information and Communication, ID : 10670/1.lz1cnt
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Résumé
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 na'ive 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.