A MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING-BASED SMISHING DETECTION MODEL FOR MOBILE MONEY TRANSACTIONS

As mobile services proliferate to include financial transactions, the threat of phishing attacks targeting users has equally been escalating. Attackers have been using different kinds of phishing techniques, especially in third world where mobile services are prevalent. As such, this paper presents a Smishing (SMS phishing) Detection model leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques. It aims to detect smishing threats in real-time by the integrating NLP with ML The developed model harnesses NLP algorithms to analyse text- based messages, scrutinizing linguistic patterns and contextual clues indicative of smishing attempts. Through ML algorithms, the model learns to distinguish between legitimate (Non-Smishing) and fraudulent messages (Smishing), adapting dynamically to evolving smishing tactics. The model's efficacy is evaluated through comprehensive testing, demonstrating promising results of precision, recall, and accuracy with F-1 measure at 0.902 and AUC at 0.95. The Model stands as a proactive defence mechanism against smishing in mobile money environments, contributing to enhanced user security and trust in financial transactions.