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InfoResearchPeer-reviewed
Evaluation of Phishing Attacks Targeting Local Systems Using an Attribute-Based Dataset and Machine Learning Methods
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Summary
This study introduces an attribute-based dataset of 31 URL- and similarity-derived features for detecting phishing attacks that target local systems. Five classifiers, including Decision Tree, Random Forest, Logistic Regression, Support Vector Machine and Artificial Neural Network, were compared across three feature-engineered datasets. Logistic Regression performed best at 96.40% accuracy, and 97.48% on a public Kaggle dataset.