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Malicious URL Detection Based on Associative Classification

Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive informati...

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Detalles Bibliográficos
Autores principales: Kumi, Sandra, Lim, ChaeHo, Lee, Sang-Gon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911559/
https://www.ncbi.nlm.nih.gov/pubmed/33572521
http://dx.doi.org/10.3390/e23020182
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author Kumi, Sandra
Lim, ChaeHo
Lee, Sang-Gon
author_facet Kumi, Sandra
Lim, ChaeHo
Lee, Sang-Gon
author_sort Kumi, Sandra
collection PubMed
description Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates.
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spelling pubmed-79115592021-02-28 Malicious URL Detection Based on Associative Classification Kumi, Sandra Lim, ChaeHo Lee, Sang-Gon Entropy (Basel) Article Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates. MDPI 2021-01-31 /pmc/articles/PMC7911559/ /pubmed/33572521 http://dx.doi.org/10.3390/e23020182 Text en © 2021 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kumi, Sandra
Lim, ChaeHo
Lee, Sang-Gon
Malicious URL Detection Based on Associative Classification
title Malicious URL Detection Based on Associative Classification
title_full Malicious URL Detection Based on Associative Classification
title_fullStr Malicious URL Detection Based on Associative Classification
title_full_unstemmed Malicious URL Detection Based on Associative Classification
title_short Malicious URL Detection Based on Associative Classification
title_sort malicious url detection based on associative classification
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7911559/
https://www.ncbi.nlm.nih.gov/pubmed/33572521
http://dx.doi.org/10.3390/e23020182
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