A comparison study of machine learning techniques for phishing detection

نویسندگان

چکیده

In the last few years, phishing attacks have been increasing eventually. As internet is developing, security for it becoming a challenging task. Cyber-attacks and threats are rapidly. These days many fake websites created to deceive victims by collecting their login credentials, bank details, etc. Many anti-phishing products launched into market use blacklists, heuristics, visual machine learning-based approaches, these cannot prevent all attacks. However, unlike predicting URLs, there only studies that compare learning techniques in phishing. The present study compares predictive accuracy of several methods including Decision tree, Random forest, Multilayer Perceptions, Support Vector Machines, XGBoost URLs.

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ژورنال

عنوان ژورنال: Journal of Business and Information Systems

سال: 2022

ISSN: ['2685-2543']

DOI: https://doi.org/10.36067/jbis.v4i1.120