Abstract
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.
Highlights
We identified that 25% of malicious universal resource locators (URLs) with unique domain names had the same total number of lines of code
A dataset of benign and malicious URLs was collected from benchmark sources
The classification based on association (CBA) model achieved an accuracy rate of 95.83% with low false positive and negative rates in the classification of URLs
Summary
Malicious URL Detection Based on Associative Classification. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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