Detecting Malware Website Using Machine Learning Methods and Techniques

Abstract

A malware website is a site designed to harm users by installing malicious software (malware) on their devices, stealing data, or redirecting them to other harmful sites. A malware website spreads malware, infects the victim's system, and steals important information to harm the user. The global lockdown will see an increase in and shift toward using internet services while staying at home in 2020. As a result, businesses suffered significant data breaches and the number of cybercrimes committed by criminals increased. Malware URLs and threat types must be located in order to stop these attacks. The majority of malware web pages can be identified by static properties that describe these behaviors because they import exploits from distant resources and conceal exploit code. In recent years, a number of models and approaches have been proposed to identify such phishing URLs. In this paper, a machine learning strategy based on a machine learning model for the most accurate detection of malware websites is reviewed and proposed. In addition, we carry out a reconnaissance on the URL to supply additional details regarding the port status, directories, and subdomains of the website.