Deep Learning-Based Detection of Phishing URLs Using URL Structure and Character-Level Features

Phishing attacks are one of the most targeted cybercrimes today, causing billions of dollars in losses each year. Detection systems that rely on phishing blacklists fail to capture new and evolving phishing attacks, while systems based on manually engineered features often lack the accuracy required to detect previously unseen threats. In this paper, we present an effective end-to-end deep learning solution for phishing URL detection by integrating a multi-branch neural network architecture with character embeddings and structural URL features. Our proposed model consists of a combined deep learning architecture incorporating multi-kernel CNN layers, dilated convolution layers, Bi-directional LSTM, and structural features extracted from URLs. To support real-world applicability and scalability, we developed an interactive Streamlit-based dashboard that dynamically generates and manages large-scale datasets, enabling non-static data collection and experimentation. Using this dashboard-driven dynamic data pipeline, we generated synthetic datasets containing up to 2 million URLs. The proposed model was evaluated on datasets containing 1 million URLs and achieved 99.44% accuracy, 99.21% recall, and 99.32% F1-score, significantly outperforming existing machine learning-based baselines. The model was further analyzed using decision curves, lift and gain charts, Kolmogorov–Smirnov statistics, and calibration metrics. Experimental results demonstrate strong generalization capabilities, robustness against zero-day attacks, and suitability for real-world deployment. Additionally, the model provides high interpretability through feature importance and correlation analyses.