FeUDA-BERT: Federated URL Domain-Aware BERT for Phishing URL Detection

Phishing URL classification is a critical component of cybersecurity. Although numerous phishing detection models based on machine learning and deep learning have been proposed, many face challenges related to generalisability and domain adaptation. This is largely due to the absence of representative training data and restricted threat intelligence sharing caused by data privacy and commercial concerns. To address these challenges, we introduce FeUDA-BERT, a federated, domain-aware BERT-based framework designed for privacy-preserving phishing URL detection. Our approach includes a novel URL domain-aware self-attention mechanism and a self-attention-based client selection method that tackles client data heterogeneity and enhances detection accuracy. We assess our model in a federated learning setup and show that each contribution — the domain-aware attention and the client selection strategy — independently boosts performance over baseline methods. Combined, they yield even greater improvements, achieving an average F1 score of 0.93. Using statistical analyses, we further validate that these performance gains are statistically significant, demonstrating the robustness and effectiveness of our proposed framework.