Comparison of Classification Algorithm Accuracy Levels in Phishing Detection: Decision Tree, Random Forest, and Support Vector Machine

Abstract

Phishing attacks have become a serious threat in the realm of cybersecurity, with both direct and indirect impacts on individuals and organizations. This study aims to compare the performance of three machine learning classification algorithms, namely Decision Tree, Random Forest, and Support Vector Machine, in detecting phishing cases using relevant datasets. The experimental results showed that Random Forest achieved the highest accuracy (96.675%), followed by Decision Tree (93.57%), while Support Vector Machine showed lower performance (54.724%). Cohen's Kappa also indicated that Random Forest had the highest correlation value. In conclusion, for this dataset, Random Forest and Decision Tree are more effective at detecting phishing attacks compared to Support Vector Machine