Machine Learning-Based Phishing URL Detection System Using Random Forest Algorithm Integrated with a WhatsApp Bot

Abstract

Phishing attacks based on Uniform Resource Locator (URL) links are one of the most significant cyber threats capable of exploiting user negligence at government institutions, including the Meteorology, Climatology, and Geophysics Agency (BMKG). This study aims to design and build a machine learning-based phishing URL detection system using the Random Forest algorithm integrated with the WhatsApp Bot messaging application. URL features were extracted from lexical aspects, government-specific domain knowledge, and BMKG domain typosquatting indicators. The model was trained using a dataset of 12,744 URLs consisting of 6,372 legitimate URLs and 6,372 phishing URLs with an 80:20 data split. Statistical evaluation results on the testing set (2,549 URLs) show that the Random Forest model achieves an accuracy of 98.16%, precision of 98.73%, recall of 97.57%, and F1-score of 98.14%. Functionality testing through Black Box Testing on 20 test URLs (10 legitimate and 10 phishing) produced a 100% success rate with an average response time of less than 10 seconds per URL. This integration is proven to provide a practical, responsive, and precise early detection solution for BMKG employees.