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), 2024)
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.