An Integrated NLP and Machine Learning Model for Detecting Smishing Attacks on Mobile Money Platforms

Abstract

The Southern African Development Community (SADC), notably Zambia, has experienced a rise in mobile financial services, which has increased vulnerability to SMSphishing attacks leading to financial losses which has had negative socio-economic effects. This paper presents the cybersecurity risks associated with SMS-phishing on mobile money platforms and proposes a detection model using machine learning (ML) and natural language processing (NLP). The model employs Random Forest and Naïve Bayes algorithms for classification, utilizing NLP techniques such as Named Entity Recognition and part-of-speech tagging to extract relevant text features from SMS messages. The model is trained on both real-world and synthetic SMS datasets consisting of Bemba and English, with performance evaluated using precision, recall, F1 score, and ROC curves. Initial results demonstrate high accuracy and effective detection capabilities. The paper also stresses the need for user education to complement the technological solution in enhancing mobile financial security