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A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators

Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method fo...

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Autores principales: Aldakheel, Eman Abdullah, Zakariah, Mohammed, Gashgari, Ghada Abdalaziz, Almarshad, Fahdah A., Alzahrani, Abdullah I. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181756/
https://www.ncbi.nlm.nih.gov/pubmed/37177607
http://dx.doi.org/10.3390/s23094403
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author Aldakheel, Eman Abdullah
Zakariah, Mohammed
Gashgari, Ghada Abdalaziz
Almarshad, Fahdah A.
Alzahrani, Abdullah I. A.
author_facet Aldakheel, Eman Abdullah
Zakariah, Mohammed
Gashgari, Ghada Abdalaziz
Almarshad, Fahdah A.
Alzahrani, Abdullah I. A.
author_sort Aldakheel, Eman Abdullah
collection PubMed
description Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate.
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spelling pubmed-101817562023-05-13 A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators Aldakheel, Eman Abdullah Zakariah, Mohammed Gashgari, Ghada Abdalaziz Almarshad, Fahdah A. Alzahrani, Abdullah I. A. Sensors (Basel) Article Organizations and individuals worldwide are becoming increasingly vulnerable to cyberattacks as phishing continues to grow and the number of phishing websites grows. As a result, improved cyber defense necessitates more effective phishing detection (PD). In this paper, we introduce a novel method for detecting phishing sites with high accuracy. Our approach utilizes a Convolution Neural Network (CNN)-based model for precise classification that effectively distinguishes legitimate websites from phishing websites. We evaluate the performance of our model on the PhishTank dataset, which is a widely used dataset for detecting phishing websites based solely on Uniform Resource Locators (URL) features. Our approach presents a unique contribution to the field of phishing detection by achieving high accuracy rates and outperforming previous state-of-the-art models. Experiment results revealed that our proposed method performs well in terms of accuracy and its false-positive rate. We created a real data set by crawling 10,000 phishing URLs from PhishTank and 10,000 legitimate websites and then ran experiments using standard evaluation metrics on the data sets. This approach is founded on integrated and deep learning (DL). The CNN-based model can distinguish phishing websites from legitimate websites with a high degree of accuracy. When binary-categorical loss and the Adam optimizer are used, the accuracy of the k-nearest neighbors (KNN), Natural Language Processing (NLP), Recurrent Neural Network (RNN), and Random Forest (RF) models is 87%, 97.98%, 97.4% and 94.26%, respectively, in contrast to previous publications. Our model outperformed previous works due to several factors, including the use of more layers and larger training sizes, and the extraction of additional features from the PhishTank dataset. Specifically, our proposed model comprises seven layers, starting with the input layer and progressing to the seventh, which incorporates a layer with pooling, convolutional, linear 1 and 2, and linear six layers as the output layers. These design choices contribute to the high accuracy of our model, which achieved a 98.77% accuracy rate. MDPI 2023-04-30 /pmc/articles/PMC10181756/ /pubmed/37177607 http://dx.doi.org/10.3390/s23094403 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Aldakheel, Eman Abdullah
Zakariah, Mohammed
Gashgari, Ghada Abdalaziz
Almarshad, Fahdah A.
Alzahrani, Abdullah I. A.
A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title_full A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title_fullStr A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title_full_unstemmed A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title_short A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators
title_sort deep learning-based innovative technique for phishing detection in modern security with uniform resource locators
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181756/
https://www.ncbi.nlm.nih.gov/pubmed/37177607
http://dx.doi.org/10.3390/s23094403
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