SmishShield: A Machine Learning-Based Smishing Detection System

The surge in mobile device adoption has led to an increase in cyber threats, particularly Short Message Service (SMS) phishing (smishing), which poses significant risks to personal and financial security. To address this growing concern in the context of Ghana, we have introduced Smishield—a machine-learning-based smishing detection system. By utilizing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization, SMS messages are converted into numerical representations, effectively capturing their underlying meaning. Our evaluation encompasses a variety of classification algorithms, such as Support Vector Machines, Logistic Regression, Decision Trees, and Random Forest, while considering different test and training ratios. Through thorough assessment, the Random Forest Tree algorithm emerges as the optimal candidate, boasting remarkable statistics of 99.47% accuracy, 98.91% precision, and an impressive Area Under the Curve (AUC) score of 99.95%. In the context of our study, the model derived from our efforts is named SmishShield. We conducted a comprehensive evaluation by benchmarking it against other models, and the results demonstrate its notable performance.