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The rise of cybercrime has motivated the need for improved early detection and prediction mechanisms to prevent cyber-attacks from causing damage to unsuspecting victims. We developed and analyzed various machine learning algorithms to tackle one approach for early detection, URL classification. Unlike previous research, which focused on binary classification, our approach focuses on classifying URLs to their likely attack category. Through testing and evaluation, we found that ensemble methods perform the best with our optimal feature set, producing accuracies as high as 95%.
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