Comparative Analysis of Machine Learning Algorithms for Phishing Email Detection

Abstract

Nowadays,The danger of cyberattacks grows as technology develops, requiring more advanced detection and prevention methods. With an emphasis on e-mail phishing detection, the study explores the use of machine learning (ML) to improve cybersecurity measures. Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), and CatBoost are among the ML models that are assessed to determine how well they can differentiate between secure and phishing e-mails. F1-score, recall, accuracy, and precision are among the evaluation measures. The results show that all models perform well, with SVM showing perfect accuracy. These findings highlight the importance of cutting-edge technologies in strengthening cybersecurity defenses against changing cyber threats.