A Novel Approach for Phishing Website Detection using Deep Learning Models

Phishing website detection is a major task in cybersecurity, aimed at saving users from sites that steal personal information. The traditional approaches can hardly minimize false positives and cope with the dynamic strategies of phishing. In this work, a novel approach has been proposed to detect phishing websites using deep learning models, such as CNN, RNN, RNN with attention layers, CRNN, LSTM, and GRU, to enhance accuracy in detection. This work has addressed one vital domain challenge, which includes very rapid changes of techniques and phishing activities, and the real-time detection feature that conventional models generally may not have. The proposed approach will leverage these advanced models for automatic feature extraction and adaptability to new strategies for significant improvement of detection accuracy and reduction of false positives. The proposed method gives a robust and real-time solution for the task of phishing website detection and improving cybersecurity in general, after its evaluation on a comprehensive dataset.