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Chapter 4 evaluates the performance of six machine learning models for phishing URL detection, highlighting their high accuracy and effectiveness across various metrics such as precision, recall, and F1-score. Logistic Regression and Gaussian Naïve Bayes performed comparably to more complex models like Random Forest and SVM, demonstrating the importance of feature selection and preprocessing. The results indicate that these models are well-suited for real-world applications, with Random Forest and Gaussian

Author

mustafa.alsomadaey

Language

EN