<![CDATA[Phishing attacks continue to be one of the most pervasive cybersecurity threats, particularly through malicious URLs designed to mimic legitimate websites and steal sensitive user information. To address this challenge, this study employs the Random Forest algorithm for automated phishing URL detection using a publicly available dataset from Kaggle. The dataset contains diverse structural, technical, and popularity-based features that capture behavioral and lexical characteristics of each URL. Following data preprocessing and an 80/20 train–test split, the Random Forest classifier achieved strong predictive performance, attaining an accuracy of 94.94%, a precision of 95.19%, and a recall of 96.94%. The model further demonstrated robust classification capability with an F1-score of 96.06% and an ROC AUC value of 0.985, indicating excellent discrimination between phishing and legitimate URLs. Feature importance analysis shows that factors such as the URL’s presence in Google’s index, page rank metrics, and specific structural patterns significantly influence prediction outcomes. Additionally, performance visualizations including ROC and Precision–Recall curves reinforce the model’s reliability and stability. Overall, the findings suggest that Random Forest provides an effective and efficient solution for phishing URL detection, offering promising potential for integration into real-world cybersecurity systems.]]>
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