Abstract: This research focuses on evaluating whether a website is legitimate or phishing. Our research contributes to improving the accuracy of phishing website detection. Hence, a feature selection algorithm is employed and integrated with an ensemble learning methodology, which is based on majority voting, and compared with different classification models including Random forest, Logistic Regression, Prediction model etc. Our research demonstrates that current phishing detection technologies have an accuracy rate between 70% and 92.52%. The experimental results prove that the accuracy rate of our proposed model can yield up to 95%, which is higher than the current technologies for phishing website detection. Moreover, the learning models used during the experiment indicate that our proposed model has a promising accuracy rate.

Alyssa Anne Ubing, Syukrina Kamilia Binti Jasmi, Azween Abdullah, NZ Jhanjhi and Mahadevan Supramaniam. “Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning”. International Journal of Advanced Computer Science and Applications (ijacsa) 10.1 (2019). http://dx.doi.org/10.14569/IJACSA.2019.0100133

@article{Ubing2019,
title = {Phishing Website Detection: An Improved Accuracy through Feature Selection and Ensemble Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100133},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100133},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {1},
author = {Alyssa Anne Ubing and Syukrina Kamilia Binti Jasmi and Azween Abdullah and NZ Jhanjhi and Mahadevan Supramaniam}
}