@article{0f0afe755d414821934572bc8e509503,
title = "AI-driven approach for robust real-time detection of zero-day phishing websites",
keywords = "deep learning, machine learning, phishing, phishing web page, zero-day phishing web page",
author = "Thomas Nagunwa",
note = "Publisher Copyright: Copyright {\textcopyright} 2024 Inderscience Enterprises Ltd. political and social impacts on individuals, organisations and governments (Alkhalil et al., 2021; Allianz, n.d.; Ball, 2017; Brattberg and Maurer, 2018; CNN, 2020; FBI, 2018; Gendre, 2015, 2019; Greenberg, 2017; IBM Security, 2019; Internet Society, 2016; Koulopoulos, 2017; Lee and Rotoloni, 2016; Pompon, 2019; Ponemon Institute, 2015; Retruster, n.d.; Rodr{\'i}guez, 2019; SecureWorks, 2019; Sophos, 2019; Verizon, 2018).",
year = "2024",
doi = "10.1504/IJICS.2024.136735",
language = "English",
volume = "23",
pages = "79--118",
journal = "International Journal of Information and Computer Security",
issn = "1744-1765",
publisher = "Inderscience",
number = "1",
}