Machine Learning-Based Phishing Detection Using URL Features: A Comprehensive Review

Abstract

Phishing is a social engineering attack in which an attacker sends a fraudulent message to a user in the hope of obtaining sensitive confidential information. Machine learning appears to be a promising technique for phishing detection. Typically, website content and Unified Resource Locator (URL) based features are used. However, gathering website content features requires visiting malicious sites, and preparing the data is labor-intensive. Towards this end, researchers are investigating if URL-only information can be used for phishing detection. This approach is lightweight and can be installed at the client’s end, they do not require data collection from malicious sites and can identify zero-day attacks. We conduct a systematic literature review on URL-based phishing detection. We selected recent papers (2018 –) or if they had a high citation count (50+ in Google Scholar) that appeared in top conferences and journals in cybersecurity. This survey will provide researchers and practitioners with information on the current state of research on URL-based website phishing attack detection methodologies. In this survey, we have seen that even though there is a lack of a centralized dataset, algorithms like Random Forest, and Long Short-Term Memory with appropriate lexical features can detect phishing URLs effectively.