Title: The hybrid framework of ensemble technique in machine learning for phishing detection

Authors: Akanksha S. Mahajan; Pradnya K. Navale; Vaishnavi V. Patil; Vijay M. Khadse; Parikshit N. Mahalle

Addresses: Department of Computer Engineering and Information Technology, College of Engineering Pune (COEP), India ' Department of Computer Engineering and Information Technology, College of Engineering Pune (COEP), India ' Department of Computer Engineering and Information Technology, College of Engineering Pune (COEP), India ' Department of Computer Engineering and Information Technology, College of Engineering Pune (COEP), India ' Department of Artificial Intelligence and Data Science, Bansilal Ramnath Agarwal Charitable Trust, Vishwakarma Institute of Information Technology, Kondhawa (Bk), Pune, India

Abstract: The benefit of online systems has been availed by users and cybercrimes alike. Phishing has become a popular cybercrime. Phishing is a significant criminal activity for financial gains. Studies about different machine learning algorithms are a perpetual journey to detect malicious data. There are lots of algorithms proposed for detecting a phishing website. The selection of the best solution for the problem is not an easy task in a phishing domain. In this study, the focus is on experimental study of ensemble learning methods, feature reduction techniques and hybrid approach. In machine learning, for improvement in performance, ensemble learning plays a crucial role. In this study, we do a comparative analysis of bagging, boosting and stacking ensemble learning models and propose a new hybrid model in the phishing domain.

Keywords: machine learning; phishing; hybrid ensemble models; ensemble techniques; feature reduction techniques; principal component analysis; PCA; linear discriminant analysis; LDA; isometric mapping; IsoMap; reliability; computer security.

DOI: 10.1504/IJICS.2023.131099

International Journal of Information and Computer Security, 2023 Vol.21 No.1/2, pp.162 - 184

Received: 12 May 2021
Accepted: 12 Jan 2022
Published online: 26 May 2023 *

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