A Review of Feature Optimization and Explainable AI for Phishing URL Detection

Phishing URL detection has evolved from reactive blacklists and heuristics to machine learning, deep learning, ensemble, transformer and explain-able AI tech-niques. The provided presentation outlines a valuable pipeline that includes data preprocessing, feature optimization, classification comparison, ensemble learning, XAI, and traditional evaluation metrics. This framework is strengthened by recent literature that focuses on multimodal evidence, cross-dataset validation, incremen-tal learning, adversarial robustness, and deployment efficiency. This review com-bines these directions focusing on URL and webpage features, feature selection, model families, ensemble strategies, and SHAP/LIME interpretability. Even though contextual and infrastructure evidence can help improve coverage at high-er computational cost, it is still found on the most attractive features, namely URL and lexical features, for early screening. Feature-selection studies show that sig-nificant dimensionality reduction is possible; however, feature importance varies depending on the collection time and data set. Explainability has been employed to reveal signals of importance, inform feature reduction, and has become a more common approach, but the quality of explanations is less frequently assessed than the quality of predictive accuracy. However, there are significant gaps on the fol-lowing points: generalization over time, generalization across datasets, adversarial evasion, reproducible benchmarking, inference cost and common standards of ac-curacy, robustness, efficiency, and explanation fidelity. The adaptive, lightweight and explanation-aware detection systems are proposed as research directions for future.