Open Access

ARTICLE

Optimized Phishing Detection with Recurrent Neural Network and Whale Optimizer Algorithm

by Brij Bhooshan Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, 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:

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

Received 19 February 2024; Accepted 13 June 2024; Issue published 12 September 2024

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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Keywords

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