Enhancing Phishing URL Detection: A Comparative Study of Machine Learning Algorithms
Ayoub Alsarhan, Bashar Igried, Raad Mohammad Bani Saleem, Mohammad Alauthman, Mohammad Aljaidi · 2023
Phishing constitutes a significant threat in the digital world, often exploiting human vulnerabilities to illicitly obtain sensitive data such as personal credentials, financial details, and private information. Misusing this information results in substantial financial loss and personal harm to victims. This study introduces an innovative approach to mitigate the risk of phishing attacks by employing machine learning algorithms to detect phishing URLs. The proposed method applies a suite of algorithms, including j48, Naïve Bayes, JRip, and Decision Table, to a robust dataset of 11,430 URLs, each with 87 extracted features. The results reveal the considerable potential of machine learning in identifying phishing threats. Furthermore, the study explores a novel server-side analysis concept where the server scrutinizes links transmitted via emails or social communication platforms such as WhatsApp, Messenger, and Instagram. The application of phishing detection algorithms filters and prevents the delivery of phishing links, thus reducing the potential harm to users. This research is poised to significantly contribute to cybersecurity by enhancing phishing detection mechanisms' accuracy and efficiency.