Real-Time Phishing URL Detection by using XGBoost and Google Safe Browsing API

This research article presents a comprehensive phishing URL detection system based on a multifaceted approach, which integrates a whitelist filter, rule-based analysis, machine learning-based classification, and a connection to the Google Safe Browsing API. The system initially checks a URL against an approved database list of legitimate websites, and then inspects the implementation of the security protocol with an emphasis on HTTPS. For URLs that are not recognized as valid by whitelist filtering, the system utilizes machine learning algorithms to analyze URL properties and estimate the likelihood of a phishing attack. A risk scoring mechanism modifies confidence scores according to identified security vulnerabilities. Experimental results show the effectiveness of the system in detecting phishing URLs with minimal false positives. The architecture of the system offers strong protection against phishing attacks, and thus, it is deployable in real-world web browsers and email security systems.