An optimized stacking ensemble model for phishing websites detection

Al-Sarem, M., Saeed, F., Al-Mekhlafi, Z.G., Mohammed, B.A., Al-Hadhrami, T. ORCID: 0000-0001-7441-604X, Alshammari, M.T., Alreshidi, A. and Alshammari, T.S., 2021. An optimized stacking ensemble model for phishing websites detection. Electronics, 10 (11): 1285. ISSN 2079-9292

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Abstract

Security attacks on legitimate websites to steal users’ information, known as phishing attacks, have been increasing. This kind of attack does not just affect individuals’ or organisations’ websites. Although several detection methods for phishing websites have been proposed using machine learning, deep learning, and other approaches, their detection accuracy still needs to be enhanced. This paper proposes an optimized stacking ensemble method for phishing website detection. The optimisation was carried out using a genetic algorithm (GA) to tune the parameters of several ensemble machine learning methods, including random forests, AdaBoost, XGBoost, Bagging, GradientBoost, and LightGBM. The optimized classifiers were then ranked, and the best three models were chosen as base classifiers of a stacking ensemble method. The experiments were conducted on three phishing website datasets that consisted of both phishing websites and legitimate websites—the Phishing Websites Data Set from UCI (Dataset 1); Phishing Dataset for Machine Learning from Mendeley (Dataset 2, and Datasets for Phishing Websites Detection from Mendeley (Dataset 3). The experimental results showed an improvement using the optimized stacking ensemble method, where the detection accuracy reached 97.16%, 98.58%, and 97.39% for Dataset 1, Dataset 2, and Dataset 3, respectively

Item Type: Journal article
Publication Title: Electronics
Creators: Al-Sarem, M., Saeed, F., Al-Mekhlafi, Z.G., Mohammed, B.A., Al-Hadhrami, T., Alshammari, M.T., Alreshidi, A. and Alshammari, T.S.
Publisher: MDPI
Date: 28 May 2021
Volume: 10
Number: 11
ISSN: 2079-9292
Identifiers:
NumberType
10.3390/electronics10111285DOI
1443108Other
Rights: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 04 Jun 2021 13:53
Last Modified: 04 Jun 2021 13:53
URI: http://irep.ntu.ac.uk/id/eprint/42984

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