Optimized Phishing Detection Through URL Analysis by a Gradient Boosting-RNN Ensemble Model

Phishing attacks pose a major threat to cybersecurity by deceiving users into revealing sensitive information. This study proposes an optimized phishing detection framework that leverages both syntactic URL features and temporal behavior through a novel ensemble model combining Gradient Boosting and Recurrent Neural Networks (RNN). The Gradient Boosting component captures static patterns and feature importance from structured URL attributes, while the RNN processes sequential character-level URL data to learn underlying patterns of malicious domains. The ensemble approach enhances detection accuracy, reduces false positives, and adapts effectively to evolving phishing tactics. Experimental results on benchmark datasets demonstrate superior performance compared to individual models, confirming the potential of hybrid ensemble techniques for real-time phishing URL detection.