Phishing URL Detection Using Deep Learning: A Resilient Approach to Mitigating Emerging Cybersecurity Threats

Phishing via malicious URLs remains a significant cybersecurity threat, exacerbated by the increasing dependence on digital platforms for communication, transactions, and data exchange.The ability to accurately distinguish between legitimate and phishing URLs is critical for safeguarding sensitive information and mitigating cyber threats.This study proposes a deep learning-based phishing URL detection model that processes raw URL input without requiring manual feature engineering.The model integrates char-acter-level embeddings with a hybrid parallel CNN-BiGRU architecture, leveraging Parallel CNN layers for local pattern extraction and BiGRU for capturing sequential dependencies in URL structures.The experimental results demonstrate that the proposed model achieves 98.46% accuracy, an AUC curve score of 99.62%, precision 98.45%, and recall 98.46%, along with a F1-score 98.45%.The hybrid architecture outperforms the utilization of individual CNNs since it combines parallel convolutional layers for local features as well as BiGRU for sequential relationships to offer more balanced and global performance on all of the measure metrics surpassing the performance of existing phishing detection frameworks.These findings underscore the effectiveness of combining convolutional and recurrent neural networks to enhance phishing detection capabilities.The study contributes to advancing cybersecurity defenses by providing an efficient, reliable, and scalable deep learning-based phishing detection framework capable of adapting to evolving phishing tactics.