PhishGuard: A Machine Learning Framework for Windows-Specific Phishing Detection

Abstract: Phishing remains one of the most prevalent and evolving cybersecurity threats, exploiting humanvulnerabilities through deceptive digital communication. This study proposes a dynamic, Windows-specific phishing detection model leveraging Random Forest machine learning techniques. By integrating Term Frequency–Inverse Document Frequency (TF-IDF) vectorization with structured email features, the model classifies phishing and legitimate emails with high accuracy. Using secondary data and publicly available datasets, the model achieved a classification accuracy of 98.31% and demonstrated balanced performance across precision, recall, and F1-score metrics. This research underscores the effectiveness of hybrid feature strategies and ensemble learning for phishing detection while outlining key limitations and future directions, including model generalization and real-world deployment readiness.