Across the Spectrum In-Depth Review AI-Based Models for Phishing Detection

Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly significant among web phishing techn...

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Bibliographic Details
Published in:	IEEE open journal of the Communications Society p. 1
Main Authors:	Ahmad, Shakeel, Zaman, Muhammad, AL-Shamayleh, Ahmad Sami, Kehkashan, Tanzila, Ahmad, Rahiel, Abdulhamid, Safi' I Muhammad, Ergen, Ismail, Akhunzada, Adnan
Format:	Journal Article
Language:	English
Published:	IEEE 16-09-2024
Subjects:	Accuracy Anomaly Detection Biological system modeling Blocklists Computational modeling Computer security Cyber-Attack Mitigation Cybersecurity Deep Learning (DL) Electronic mail Machine Learning (ML) Phishing Phishing Detection Threat Intelligence Web Phishing Detection Whitelists
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Summary:	Advancement of the Internet has increased security risks associated with data protection and online shopping. Several techniques compromise Internet security, including hacking, SQL injection, phishing attacks, and DNS tunneling. Phishing attacks are particularly significant among web phishing techniques. In a phishing attack, the attacker creates a fake website that closely resembles a legitimate one to deceive users into providing sensitive information. These attacks can be detected using both traditional and modern AI-based models. However, even with state-of-the-art methods, accurately classifying newly emerged links as phishing or legitimate remains a challenge. This study conducts a comparative analysis of more than 130 articles published between 2020 and 2024, identifying challenges and gaps in the literature and comparing the findings of various authors. The novelty of this research lies in providing a roadmap for researchers, practitioners, and cybersecurity experts to navigate the landscape of machine learning (ML) and deep learning (DL) models for phishing detection. The study reviews traditional phishing detection methods, ML and DL models, phishing datasets, and the step-by-step phishing process. It highlights limitations, research gaps, weaknesses, and potential improvements. Accuracy measures are used to compare model performance. In conclusion, this research provides a comprehensive survey of website phishing detection using AI models, offering a new roadmap for future studies.
ISSN:	2644-125X 2644-125X
DOI:	10.1109/OJCOMS.2024.3462503