Open Access

ARTICLE

A Hybrid CNN-Brown-Bear Optimization Framework for Enhanced Detection of URL Phishing Attacks

by Brij B. Gupta^1,*, Akshat Gaurav², Razaz Waheeb Attar³, Varsha Arya⁴, Shavi Bansal⁵, Ahmed Alhomoud⁶, Kwok Tai Chui⁷

1 Department of Computer Science and Information Engineering, Asia University, Taichung, 413, Taiwan
2 Computer Engineering, Ronin Institute, Montclair, NJ 07043, USA
3 Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
4 Department of Business Administration, Asia University, Taichung, 413, Taiwan
5 Department of Research and Innovation, Insights2Techinfo, Jaipur, 302001, India
6 Department of Computer Science, College of Science, Northern Border University, Arar, 91431, Saudi Arabia
7 Department of Electronic Engineering and Computer Science, Hong Kong Metropolitan University (HKMU), Hong Kong, China

* Corresponding Author: Brij B. Gupta. Email:

Computers, Materials & Continua 2024, 81(3), 4853-4874. https://doi.org/10.32604/cmc.2024.057138

Received 09 August 2024; Accepted 26 November 2024; Issue published 19 December 2024

Abstract

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial services. After the first reported incident in 1995, its impact keeps on increasing. Also, during COVID-19, due to the increase in digitization, there is an exponential increase in the number of victims of phishing attacks. Many deep learning and machine learning techniques are available to detect phishing attacks. However, most of the techniques did not use efficient optimization techniques. In this context, our proposed model used random forest-based techniques to select the best features, and then the Brown-Bear optimization algorithm (BBOA) was used to fine-tune the hyper-parameters of the convolutional neural network (CNN) model. To test our model, we used a dataset from Kaggle comprising 11,000+ websites. In addition to that, the dataset also consists of the 30 features that are extracted from the website uniform resource locator (URL). The target variable has two classes: “Safe” and “Phishing.” Due to the use of BBOA, our proposed model detects malicious URLs with an accuracy of 93% and a precision of 92%. In addition, comparing our model with standard techniques, such as GRU (Gated Recurrent Unit), LSTM (Long Short-Term Memory), RNN (Recurrent Neural Network), ANN (Artificial Neural Network), SVM (Support Vector Machine), and LR (Logistic Regression), presents the effectiveness of our proposed model. Also, the comparison with past literature showcases the contribution and novelty of our proposed model.

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