Advanced AI-Powered Phishing URL Detection Using Ensemble Algorithm with RNN, BILSTM, GRU Approach

Phishing attacks represent a major security threat to cybersecurity, often tricking users into divulging sensitive information through malicious URLs. To combat this growing concern, our project, Advanced Phishing URL Detection Through AI Techniques, leverages deep learning-based models to classify URLs as either phishing or Authentic. Utilizing the ISCX-URL dataset from the Canadian Institute for Cybersecurity, we explore the effectiveness of various recurrent neural network (RNN) architectures for phishing URL detection. We implemented and evaluated three deep learning models—Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory (BiLSTM). Our experimental results indicate that the BiLSTM model outperformed the others, achieving a test accuracy of 90%, followed by GRU at 87% and RNN at 84%. These findings suggest that incorporating bidirectional context awareness significantly enhances phishing detection accuracy. This research highlights the potential of AI-driven methods in strengthening cybersecurity defences against phishing attacks.In this work, we present a comparative study of deep learning models—RNN, GRU, and BiLSTM—for phishing URL detection using the ISCX-URL dataset. Our primary contribution lies in the empirical demonstration that BiLSTM, due to its bidirectional context awareness, achieves the highest test accuracy of 90.14%, outperforming GRU 87.10% and RNN 84.00%. Additionally, we apply preprocessing techniques and experiment with model tuning to enhance classification accuracy. These findings underscore the applicability of deep learning for real-time phishing detection in cybersecurity.