Website Phishing Detection Using Machine Learning Techniques

Authors

R. Alazaidah, Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, JordanFollow
A. Al-Shaikh, Department of Cyber Security, Faculty of Information Technology, Zarqa University, Zarqa, JordanFollow
M. R. AL-Mousa, Department of Cyber Security, Faculty of Information Technology, Zarqa University, Zarqa, JordanFollow
H. Khafajah, Department of Data Science and Artificial Intelligence, Faculty of Information Technology, Zarqa University, JordanFollow
G. Samara, Department of Computer Science, Faculty of Information Technology, Zarqa University, Zarqa, JordanFollow
M. Alzyoud, Department of Computer Science, Faculty of Information Technology, Al al-Bayt University, Mafraq, JordanFollow

Author Country (or Countries)

Jordan

Abstract

Phishing is a cybercrime that is constantly increasing in the recent years due to the increased use of the Internet and its applications. It is one of the most common types of social engineering that aims to disclose or steel users sensitive or personal information. In this paper, two main objectives are considered. The first is to identify the best classifier that can detect phishing among twenty-four different classifiers that represent six learning strategies. The second objective aims to identify the best feature selection method for websites phishing datasets. Using two datasets that are related to Phishing with different characteristics and considering eight evaluation metrics, the results revealed the superiority of RandomForest, FilteredClassifier, and J-48 classifiers in detecting phishing websites. Also, InfoGainAttributeEval method showed the best performance among the four considered feature selection methods.

Digital Object Identifier (DOI)

https://dx.doi.org/10.18576/jsap/130108

Recommended Citation

Alazaidah, R.; Al-Shaikh, A.; R. AL-Mousa, M.; Khafajah, H.; Samara, G.; and Alzyoud, M. (2024) "Website Phishing Detection Using Machine Learning Techniques," Journal of Statistics Applications & Probability: Vol. 13: Iss. 1, Article 8.
DOI: https://dx.doi.org/10.18576/jsap/130108
Available at: https://digitalcommons.aaru.edu.jo/jsap/vol13/iss1/8