MALWARE WEBSITE DETECTION USING MACHINE LEARNING

Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. The word “malware” refers to an intent to harm. In order to harm the end user, a malware website spreads malware, infects the victim’s system, and steals important information. In the year 2020, the global lockdown saw an increase in and shift toward using internet services as a mode of operation while staying at home. This, in turn, led to an increase in the number of cybercrimes committed by criminals and significant data breaches suffered by businesses. In order to stop these attacks, malware URLs and threat types must be located. Static properties that describe these behaviours can be used to identify the vast majority of malware web pages because they import exploits from distant resources and conceal exploit code. To identify such phishing URLs, a number of models and methods have been proposed in recent years. The previous research is reviewed and a machine learning strategy for the most accurate detection of malware websites using a machine learning model is proposed in this project . In addition, we conduct a reconnaissance on the URL to provide additional information regarding the website’s subdomains, directories, and port status.