SmishGuard: Leveraging Machine Learning and Natural Language Processing for Smishing Detection

Abstract

SMS facilitates the transmission of concise text messages between mobile phone users, serving a range of functions in personal and business domains such as appointment confirmation, authentication, alerts, notifications, and banking updates. It plays a vital role in daily communication due to its accessibility, reliability, and compatibility. However, SMS unintentionally generates an environment where smishing can occur. This is because SMS is extensively available and reliable. Smishing attackers exploit this trust to trick victims into divulging sensitive information or performing malicious actions. Early detection saves users from being victimized. Researchers introduced different methods for accurately detecting smishing attacks. Machine Learning models, coupled with Language Processing methods, are promising approaches for combating the escalating menace of SMS phishing attacks by analyzing large datasets of SMS messages to differentiate between legitimate and fraudulent messages. This paper presents two methods (SmishGaurd) to detect smishing attacks that leverage machine learning models and language processing techniques. The results indicate that TF-IDF with the LDA method outperforms Weight Average Word2Vec in precision and F1-Score, and Random Forest and Extreme Gradient Boosting demonstrate higher accuracy.