Phishguard AI: Real-Time Phishing URL Detection using Explainable AI and Community Threat Intelligence

Abstract

The rapid growth of digital services in banking, e-commerce, education, and government sectors has significantly increased phishing attacks. Traditional blacklist-based detection systems are ineffective against zero-day phishing domains that imitate legitimate websites. To address this challenge, this paper proposes PHISHGUARD AI, a real-time phishing URL detection framework integrating a hyperparameter-optimized XGBoost classifier with explainable artificial intelligence (XAI) and community-driven threat intelligence. The framework utilizes lexical and structural features from URLs to calculate phishing probability using a tuned XGBoost classifier. Evaluation results on the PhiUSIIL dataset $(\mathbf{N}=\mathbf{2 3 5, 7 9 5})$ achieved 94.0% accuracy, 94.02% precision, 94.00% recall, and 94.01% F1-score. The explainability module improves transparency by identifying influential features responsible for each prediction. Finally, a community-based threat intelligence program enables users to validate and incorporate previously user-reported URLs into their machine learning models through continuous retraining cycles. Evaluation of the experimental data from this framework demonstrated a high detection accuracy with improved generalization for newly emerging phishing attacks. The proposed framework provides scalable and proactive phishing detection with improved interpretability.