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

De Rosal Ignatius Moses Setiadi; Suyud Widiono; Achmad Nuruddin Safriandono; Setyo Budi

doi:10.62411/faith.2024-15

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

Authors

De Rosal Ignatius Moses Setiadi Dian Nuswantoro University https://orcid.org/0000-0001-6615-4457
Suyud Widiono University of Technology Yogyakarta https://orcid.org/0000-0001-6412-3534
Achmad Nuruddin Safriandono Sultan Fatah University https://orcid.org/0009-0008-5608-1619
Setyo Budi Dian Nuswantoro University https://orcid.org/0000-0001-7598-1614

DOI:

https://doi.org/10.62411/faith.2024-15

Keywords:

BiGRU, Cyber attack, Cyber security, Phishing detection, Website phishing classification

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.

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Author Biographies

Suyud Widiono, University of Technology Yogyakarta

Department of Computer Engineering, Faculty of Science and Technology, University of Technology Yogyakarta, Indonesia

Achmad Nuruddin Safriandono, Sultan Fatah University

Faculty of Engineering, Sultan Fatah University, Demak, Central Java 59516, Indonesia

Setyo Budi, Dian Nuswantoro University

Information System Department, Faculty of Computer Science, Dian Nuswantoro University, Indonesia

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Published

2024-07-12

How to Cite

[1]

D. R. I. M. Setiadi, S. Widiono, A. N. Safriandono, and S. Budi, “Phishing Website Detection Using Bidirectional Gated Recurrent Unit Model and Feature Selection”, J. Fut. Artif. Intell. Tech., vol. 1, no. 2, pp. 75–83, Jul. 2024.