With the rise of mobile device usage and increasing reliance on the internet, most real-world activities—banking, shopping, and communication—have transitioned online. While this digital shift enhances convenience, it also raises cybersecurity concerns, particularly phishing attacks. Phishing involves deceptive websites mimickinglegitimateonestostealsensitive user data like passwords and credit card numbers. Traditional security tools often fail to detect such sophisticated, zero-day attacks. This study suggests a machine learnings based phishing detections system that uses algorithms such as Random Forests, SVM, Decision Tree, Naïve Bayes and Neural Networks. These models analyze URL features to classifysitesaslegitimateorphishing.The system is deployed on a free, ad-free, non- profit website that also allows users to report suspicious URLs, enhancing accuracy over time. Tested on diverse datasets, the models achieved over 90% accuracy.Thisresearchhighlightsmachine learning’sroleineffectivelycombating phishing threats and strengthening cybersecuritydefenses.
H. et al. (Mon,) studied this question.