Models in Review for the Analysis of Phishing Website URLs

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In this paper, we compare our method on DEFRAUD with current online API services and the state-of-the-art machine learning model for defending against phishing websites. Rule-based methods, in nature traditional such rules tend to get outdated quickly and are not capable of tracing new tactics used by the malicious ware every second. Over time, new proactive approaches enabled by machine learning (ML) have become more important as these solutions are flexible and adaptable in their ability to scan through modern data breaches for patterns from millions of datasets. In this study, we explore various machine learning algorithms: Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Trees (DT) Random Forest (RF), Support Vector Classifiers (SVC) and xgBoost for phishing website detection. Ensemble Methods like Random Forest, XGBoost have better accuracy/precision/recall. Metrics. While XGBoost is resource hungry, it is well known for out of the box support with huge data dimensions as well deep learning framework and avoiding overfitting. The study underscores the importance of integrating machine-learning models into practical cybersecurity applications. Future research should focus on improving these models and expanding their application across different domains to enhance cybersecurity defenses.