@inproceedings{d82cc7f0bf794808863989ae4d2f6d2a,
title = "Phishing Attack Detection Through Recursive Feature Elimination Via Cross Validation",
abstract = "Rising phishing attacks pose serious cybersecurity threats due to their use of fraudulent links to collect confidential user information. In this paper, we evaluate the performance of various Machine Learning (ML) models, including Decision Trees, Random Forest, and Extreme Gradient Boosting, to address this growing threat. Additionally, we assess the effectiveness of different feature selection techniques, such as Analysis of Variance, Correlation-based Selection, Mutual Information, and Recursive Feature Elimination with Cross-Validation. Our findings demonstrate that combining Extreme Gradient Boosting with Recursive Feature Elimination and Cross-Validation outperforms previous methods. The proposed solution achieved an accuracy of 9733 \%, a recall of 97.1656\%, an F1 score of 97.3\%, and a precision of 97.42\%, highlighting its potential for effectively identifying phishing attacks",
keywords = "Cybersecurity, Data Balancing, feature Selection, Machine Learning, Phishing Detection, URL Analysis",
author = "Salma Masmoudi and Kammoun, \{Habib M.\} and Maha Charfeddine and Bechir Hamdaoui",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 21st IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2025 ; Conference date: 12-05-2024 Through 16-05-2024",
year = "2025",
month = may,
day = "16",
doi = "10.1109/IWCMC65282.2025.11059706",
language = "English",
isbn = "979-8-3315-0888-3",
series = "International Wireless Communications And Mobile Computing Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1610--1615",
booktitle = "2025 International Wireless Communications And Mobile Computing, Iwcmc",
address = "United States",
}