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All Journal Formosa Journal of Computer and Information Science
Farah Yulianti
Universitas President
Computer Science & IT

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A Comparative Analysis of Feature Selection Methods for Machine Learning-Based Phishing URL Detection Farah Yulianti; Jessica Forenziana
Formosa Journal of Computer and Information Science Vol. 5 No. 2 (2026): August 2026
Publisher : PT FORMOSA CENDEKIA GLOBAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55927/fjcis.v5i2.16783

Abstract

This study investigates the impact of three categories of feature selection methods which are filter-based, wrapper-based, and embedded on the performance and efficiency of machine learning classifiers for phishing URL detection. Experiments were conducted on the PhiUSIIL Phishing URL Dataset comprising 235,795 URLs (134,850 legitimate, 100,945 phishing) with 20 URL structural features. Results demonstrate that LASSO (Embedded) at k = 5 achieves an F1 score of 0.9975 versus the 20-feature baseline of 0.9965 (ΔF1 = 0.001). Given that this difference falls within the per-fold standard deviation of both configurations (≤ 0.0003), we treat the two configurations as operationally equivalent and frame the contribution as efficiency at unchanged accuracy rather than as an accuracy improvement.
Co-Authors Jessica Forenziana