AN EMAIL DETECTION TOOL INTEGRATING RANDOM FOREST AND NAIVE BAYES ALGORITHMS FOR PHISHING PROTECTION

Abstract

Phishing attacks pose a significant threat to organizational email security, especially small and medium sized organizations, exploiting human vulnerabilities to steal sensitive information. This study develops a phishing detection tool that integrates Random Forest and Naive Bayes algorithms in a hybrid model to enhance detection accuracy. The tool analyzes email headers, content, and URLs, providing actionable insights for users or IT teams. Through dataset training and testing, the hybrid model achieved 96.81% accuracy, outperforming individual models. The proposed solution includes a user-friendly GUI and CLI, with features like URL screenshot previews and report generation. Nevertheless, the project will deliver a user-friendly security tool which strengthens email protection while decreasing phishing risks to safeguard organizational sensitive data. The project supports Sustainable Development Goal (SDG) 9: Industry, Innovation, and Infrastructure through its promotion of safe email communication security for organizations.