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