<![CDATA[Phishing attacks have become one of the most rapidly increasing cybersecurity threats in recent years. Phishing websites are designed to deceive users into divulging sensitive information such as login credentials, credit card data, and other personal details. This research proposes the implementation of the Random Forest algorithm for automated phishing website detection. The dataset used in this study comprises 10,000 classified URL samples, with 49 distinct features extracted. The research methodology includes data preprocessing, URL feature extraction, Random Forest model training, and performance evaluation. The evaluation results demonstrate that the developed Random Forest model achieved an accuracy of 98.20%, precision of 98.22%, recall of 98.22%, and an F1-score of 98.22%. This study proves that the Random Forest algorithm is highly effective for phishing detection and can be implemented as a preventive security system in internet Browse.]]>
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