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A Hybrid CNN-brown-bear optimization Framework for Enhanced Detection of URL Phishing Attacks

Computers, Materials & Continua 2024年第12期81卷 4853-4874页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Shavi Bansal Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Research and Innovation Insights2TechinfoJaipur302001India Department of Computer Science College of ScienceNorthern Border UniversityArar91431Saudi Arabia Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University(HKMU)Hong KongChina

Phishing attacks are more than two-decade-old attacks that attackers use to steal passwords related to financial *** the first reported incident in 1995,its impact keeps on ***,during COVID-19,due to the increase in digitization,there is an exponential increase in the number of victims of phishing *** deep learning and machine learning techniques are available to detect phishing ***,most of the techniques did not use efficient optimization *** 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)*** test our model,we used a dataset from Kaggle comprising 11,000+*** 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 ***,the comparison with past literature showcases the contribution and novelty of our proposed model.

关键词： Phishing attack CNN brown-bear optimization

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