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All Journal Ceddi Journal of Information System and Technology (JST)
Olalere, Morufu
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Computer Science & IT Decision Sciences, Operations Research & Management

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Evaluating Deep Learning Models for Website Phishing Attack Detection: A Comparative Analysis Raji Egigogo, Abdullahi; Ismaila Idris; Olalere, Morufu; Opeyemi Aderiike, Abisoye
Ceddi Journal of Information System and Technology (JST) Vol. 3 No. 2 (2024): December
Publisher : Yayasan Cendekiawan Digital Indonesia (CEDDI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56134/jst.v3i2.100

Abstract

Phishing attacks remain a significant security threat in cyberspace, targeting individuals and businesses to steal confidential information. Traditional detection methods often struggle to identify newly created or altered phishing sites, highlighting the need for more adaptive solutions. This study evaluates the performance of various deep learning (DL) models for detecting online phishing attacks. A comparative analysis of single and hybrid DL models, including CNN, LSTM, BiGRU, and their combinations, is conducted. The evaluation is based on metrics such as accuracy, precision, recall, and F1-score, derived from 17 peer-reviewed publications published between 2019 and 2024. Results indicate that hybrid models, particularly ODAE-WPDC, exhibit superior performance, achieving accuracy rates of up to 99.28% and robust results across all metrics. Single models, such as CNN and BiGRU, also demonstrate strong performance, with accuracy ranging from 97% to 99.5%. This research underscores the efficacy of deep learning architectures in phishing detection and offers practical guidance for selecting optimal models based on specific requirements.
Co-Authors Ismaila Idris Opeyemi Aderiike, Abisoye Raji Egigogo, Abdullahi