Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection

Abstract

Phishing attacks continue to be a significant threat to internet users, necessitating the development of advanced detection systems. This study explores the efficacy of a Bidirectional Gated Recurrent Unit (BiGRU) model combined with feature selection techniques for detecting phishing websites. The dataset used for this research is sourced from the UCI Machine Learning Repository, specifically the Phishing Websites dataset. This approach involves cleaning and preprocessing the data, then normalizing features and employing feature selection to identify the most relevant attributes for classification. The BiGRU model, known for its ability to capture temporal dependencies in data, is then applied. To ensure robust evaluation, we utilized cross-validation, dividing the data into five folds. The experimental results are highly promising, demonstrating a Mean Accuracy, Mean Precision, Mean Recall, Mean F1 Score, and Mean AUC of 1.0. These results indicate the model's exceptional performance distinguishing between phishing and legitimate websites. This study highlights the potential of combining BiGRU models with feature selection and cross-validation to create highly accurate phishing detection systems, providing a reliable solution to enhance cybersecurity measures.