Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study

Ammar Almomani, Mohammad Alauthman, Mohd Taib Shatnawi, Mohammed Alweshah, Ayat Alrosan, Waleed Alomoush, Brij B. Gupta, Brij B. Gupta and Brij B. Gupta
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Ammar Almomani: Research and Innovation Department, Skyline University College, UAE & IT Department, Al-Huson University College, Al-Balqa Applied University, Jordan
Mohammad Alauthman: University of Petra, Jordan
Mohd Taib Shatnawi: Al-Balqa Applied University, Jordan
Mohammed Alweshah: Al-Balqa Applied University, Jordan
Ayat Alrosan: School of Information Technology, Skyline University College, UAE
Waleed Alomoush: School of Information Technology, Skyline University College, UAE
Brij B. Gupta: Department of Computer Engineering, Kurukshetra, India
Brij B. Gupta: Asia University, Taichung, Taiwan
Brij B. Gupta: Department of Computer Engineering, National Institute of Technology Kurukshetra, India & Asia University, Taiwan

International Journal on Semantic Web and Information Systems (IJSWIS), 2022, vol. 18, issue 1, 1-24

Abstract: The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers.

Date: 2022
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