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Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

Brij Bhooshan Gupta1,2,3,*, Akshat Gaurav4, Razaz Waheeb Attar5, Varsha Arya6,7, Ahmed Alhomoud8, Kwok Tai Chui9

1 Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan
2 Symbiosis Centre for Information Technology (SCIT), Symbiosis International University, Pune, Maharashtra, 411057, India
3 Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, 248007, India
4 Ronin Institute, Montclair, NJ 07043, USA
5 Management Department, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
6 Department of Business Administration, Asia University, Taichung, 413, Taiwan
7 Department of Electrical and Computer Engineering, Lebanese American University, Beirut, 1102, Lebanon
8 Department of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia
9 Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU), Homantin, Hong Kong, China

* Corresponding Author: Brij Bhooshan Gupta. Email: email

Computers, Materials & Continua 2024, 80(3), 4895-4916. https://doi.org/10.32604/cmc.2024.050815

Abstract

Phishing attacks present a persistent and evolving threat in the cybersecurity land-scape, necessitating the development of more sophisticated detection methods. Traditional machine learning approaches to phishing detection have relied heavily on feature engineering and have often fallen short in adapting to the dynamically changing patterns of phishing Uniform Resource Locator (URLs). Addressing these challenge, we introduce a framework that integrates the sequential data processing strengths of a Recurrent Neural Network (RNN) with the hyperparameter optimization prowess of the Whale Optimization Algorithm (WOA). Our model capitalizes on an extensive Kaggle dataset, featuring over 11,000 URLs, each delineated by 30 attributes. The WOA’s hyperparameter optimization enhances the RNN’s performance, evidenced by a meticulous validation process. The results, encapsulated in precision, recall, and F1-score metrics, surpass baseline models, achieving an overall accuracy of 92%. This study not only demonstrates the RNN’s proficiency in learning complex patterns but also underscores the WOA’s effectiveness in refining machine learning models for the critical task of phishing detection.

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APA Style
Gupta, B.B., Gaurav, A., Attar, R.W., Arya, V., Alhomoud, A. et al. (2024). Optimized phishing detection with recurrent neural network and whale optimizer algorithm. Computers, Materials & Continua, 80(3), 4895-4916. https://doi.org/10.32604/cmc.2024.050815
Vancouver Style
Gupta BB, Gaurav A, Attar RW, Arya V, Alhomoud A, Chui KT. Optimized phishing detection with recurrent neural network and whale optimizer algorithm. Comput Mater Contin. 2024;80(3):4895-4916 https://doi.org/10.32604/cmc.2024.050815
IEEE Style
B.B. Gupta, A. Gaurav, R.W. Attar, V. Arya, A. Alhomoud, and K.T. Chui "Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm," Comput. Mater. Contin., vol. 80, no. 3, pp. 4895-4916. 2024. https://doi.org/10.32604/cmc.2024.050815



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
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.
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