A SMISHING ATTACK DETECTION MODEL FOR MOBILE MONEY BASED ON NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING

Abstract

As mobile money services proliferate, the threat of smishing attacks targeting users has escalated. This paper presents a Smishing Detection Leveraging Natural Language Processing (NLP) and Machine Learning (ML) techniques. It aims to detect smishing threats in real-time with the integration of an Android App. The model harnesses NLP algorithms to analyse textbased messages, scrutinizing linguistic patterns and contextual cues indicative of smishing attempts. Through ML algorithms, the model learns to distinguish between legitimate (NonSmishing) and fraudulent messages (Smishing), adapting dynamically to evolving smishing tactics. The model's efficacy is evaluated through comprehensive testing, demonstrating promising accuracy, precision, and recall rates. The Model stands as a proactive defense mechanism against smishing in mobile money environments, contributing to enhanced user security and trust in financial transactions.