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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/23/2/182 |
| _version_ | 1797417581069467648 |
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| author | Sandra Kumi ChaeHo Lim Sang-Gon Lee |
| author_facet | Sandra Kumi ChaeHo Lim Sang-Gon Lee |
| author_sort | Sandra Kumi |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:20:46Z |
| format | Article |
| id | doaj.art-6a302cd60ad84dfd8cff4d28fe006b75 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T06:20:46Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-6a302cd60ad84dfd8cff4d28fe006b752023-12-03T11:48:08ZengMDPI AGEntropy1099-43002021-01-0123218210.3390/e23020182Malicious URL Detection Based on Associative ClassificationSandra Kumi0ChaeHo Lim1Sang-Gon Lee2Department of Information Security, Dongseo University, Busan 47011, KoreaBITSCAN Co., Ltd., Seoul 04789, KoreaDepartment of Information Security, Dongseo University, Busan 47011, KoreaCybercriminals 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.https://www.mdpi.com/1099-4300/23/2/182data miningweb securitymachine learningmalicious URLsassociative classification |
| spellingShingle | Sandra Kumi ChaeHo Lim Sang-Gon Lee Malicious URL Detection Based on Associative Classification Entropy data mining web security machine learning malicious URLs 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 | data mining web security machine learning malicious URLs associative classification |
| url | https://www.mdpi.com/1099-4300/23/2/182 |
| work_keys_str_mv | AT sandrakumi maliciousurldetectionbasedonassociativeclassification AT chaeholim maliciousurldetectionbasedonassociativeclassification AT sanggonlee maliciousurldetectionbasedonassociativeclassification |