Improved Phishing Attack Detection with Machine Learning: A Comprehensive Evaluation of Classifiers and Features
Sibel Kapan,
Efnan Sora Gunal
Abstract:In phishing attack detection, machine learning-based approaches are more effective than simple blacklisting strategies, as they can adapt to new types of attacks and do not require manual updates. However, for these approaches, the choice of features and classifiers directly influences detection performance. Therefore, in this work, the contributions of various features and classifiers to detecting phishing attacks were thoroughly analyzed to find the best classifier and feature set in terms of different perfo… Show more
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