Скачать или смотреть Python Cybersecurity - Feature Selection Method for Phishing Website Classification - ClickMyProject

Phishing websites pose a significant threat to cybersecurity, exploiting deceptive tactics to steal sensitive user information. Detecting such sites remains challenging due to the absence of universally accepted identification features. This study introduces a novel Rough Set Theory-based Hybrid Feature Selection (RSTHFS) method to enhance phishing website detection using Machine Learning (ML). RSTHFS was evaluated on three datasets with 2,456, 10,000, and 88,647 instances, demonstrating an average accuracy of 95.48% while reducing feature count by 69.11%. The method was tested with LightGBM, Random Forest, and CatBoost classifiers, with CatBoost achieving the highest accuracy. Additionally, RSTHFS reduced runtime by 61.43%, significantly improving efficiency. These results highlight RSTHFS as a reliable and effective feature selection technique that enhances both the accuracy and speed of phishing detection systems, offering a valuable contribution to cybersecurity solutions.

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