Phishing websites detection by using optimized stacking ensemble model

Ghaleb Al-Mekhlafi, Z, Abdulkarem Mohammed, B, Al-Sarem, M, Saeed, F, Al-Hadhrami, T ORCID: https://orcid.org/0000-0001-7441-604X, Alshammari, MT, Alreshidi, A and Sarheed Alshammari, T, 2022. Phishing websites detection by using optimized stacking ensemble model. Computer Systems Science and Engineering, 41 (1), pp. 109-125. ISSN 0267-6192

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Abstract

Phishing attacks are security attacks that do not affect only individuals’ or organizations’ websites but may affect Internet of Things (IoT) devices and networks. IoT environment is an exposed environment for such attacks. Attackers may use thingbots software for the dispersal of hidden junk emails that are not noticed by users. Machine and deep learning and other methods were used to design detection methods for these attacks. However, there is still a need to enhance detection accuracy. Optimization of an ensemble classification method for phishing website (PW) detection is proposed in this study. A Genetic Algorithm (GA) was used for the proposed method optimization by tuning several ensemble Machine Learning (ML) methods parameters, including Random Forest (RF), AdaBoost (AB), XGBoost (XGB), Bagging (BA), GradientBoost (GB), and LightGBM (LGBM). These were accomplished by ranking the optimized classifiers to pick out the best classifiers as a base for the proposed method. A PW dataset that is made up of 4898 PWs and 6157 legitimate websites (LWs) was used for this study's experiments. As a result, detection accuracy was enhanced and reached 97.16 percent.

Item Type:	Journal article
Publication Title:	Computer Systems Science and Engineering
Creators:	Ghaleb Al-Mekhlafi, Z., Abdulkarem Mohammed, B., Al-Sarem, M., Saeed, F., Al-Hadhrami, T., Alshammari, M.T., Alreshidi, A. and Sarheed Alshammari, T.
Publisher:	Tech Science Press
Date:	2022
Volume:	41
Number:	1
ISSN:	0267-6192
Identifiers:	Number Type 10.32604/csse.2022.020414 DOI 1516687 Other
Rights:	This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	07 Feb 2022 16:43
Last Modified:	07 Feb 2022 16:43
URI:	https://irep.ntu.ac.uk/id/eprint/45536

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