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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 9, 2023.
Abstract: Phishing, a prevalent online threat where attackers impersonate legitimate organizations to obtain sensitive information from victims, poses a significant cybersecurity challenge. Recent advancements in phishing detection, particularly machine learning-based methods, have shown promising results in countering these malicious attacks. In this study, we developed and compared seven machine learning models, namely Logistic Regression (LR), k-Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), and Gradient Boosting, to assess their efficiency in detecting phishing domains. Employing the UCI phishing domains dataset as a benchmark, we rigorously evaluated the performance of these models. Our findings indicate that the Gradient Boosting-based model, in conjunction with the Random Forest, exhibits superior performance compared to the other techniques and aligns with existing solutions in the literature. Consequently, it emerges as the most accurate and effective approach for detecting phishing domains.
Kamal Omari, “Comparative Study of Machine Learning Algorithms for Phishing Website Detection” International Journal of Advanced Computer Science and Applications(IJACSA), 14(9), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140945
@article{Omari2023,
title = {Comparative Study of Machine Learning Algorithms for Phishing Website Detection},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140945},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140945},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {9},
author = {Kamal Omari}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.