Do Not Feed the Phish: Phishing Website Detection Using URL-based Features
Vielka Angela V. Adap, Gabriel A. Castillo, Erskine Jerrell M. Delos Reyes, Edward B. Ronquillo, Larry A. Vea · 2023
Due to the onset of the pandemic in previous years, industries have shifted to the use of digitalized services. Since most transactions were done online, people have become more susceptible to phishing attacks. With this, a steady rise in phishing attacks has been observed. Though cybersecurity awareness can help counter phishing attacks, improvement of existing phishing detection approaches remains desirable. Evolution of phishing websites is inevitable as phishers persist in bypassing current phishing detection approaches. This study then suggests a phishing detection model that makes use of URL-based features to detect phishing websites. Models were developed using two well-known classifiers, XGBoost and Random Forest. XGBoost classifier demonstrated the most promising performance with an accuracy of 96.51% and a kappa of 0.930.