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An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models
The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websi...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436524/ https://www.ncbi.nlm.nih.gov/pubmed/36059391 http://dx.doi.org/10.1155/2022/3241216 |
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author | Aljabri, Malak Alhaidari, Fahd Mohammad, Rami Mustafa A. Samiha Mirza, Alhamed, Dina H. Altamimi, Hanan S. Chrouf, Sara Mhd. Bachar |
author_facet | Aljabri, Malak Alhaidari, Fahd Mohammad, Rami Mustafa A. Samiha Mirza, Alhamed, Dina H. Altamimi, Hanan S. Chrouf, Sara Mhd. Bachar |
author_sort | Aljabri, Malak |
collection | PubMed |
description | The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset. |
format | Online Article Text |
id | pubmed-9436524 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-94365242022-09-02 An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models Aljabri, Malak Alhaidari, Fahd Mohammad, Rami Mustafa A. Samiha Mirza, Alhamed, Dina H. Altamimi, Hanan S. Chrouf, Sara Mhd. Bachar Comput Intell Neurosci Research Article The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset. Hindawi 2022-08-25 /pmc/articles/PMC9436524/ /pubmed/36059391 http://dx.doi.org/10.1155/2022/3241216 Text en Copyright © 2022 Malak Aljabri et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Aljabri, Malak Alhaidari, Fahd Mohammad, Rami Mustafa A. Samiha Mirza, Alhamed, Dina H. Altamimi, Hanan S. Chrouf, Sara Mhd. Bachar An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title | An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title_full | An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title_fullStr | An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title_full_unstemmed | An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title_short | An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models |
title_sort | assessment of lexical, network, and content-based features for detecting malicious urls using machine learning and deep learning models |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9436524/ https://www.ncbi.nlm.nih.gov/pubmed/36059391 http://dx.doi.org/10.1155/2022/3241216 |
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