Malicious url detection using machine learning and ensemble modeling

Websites are software applications that allow us to connect and interact with the data located in the web servers. Websites allow the user to capture, store, process, and exchange sensitive data like banking details and personal details. Web pages are accessed by merely entering the required URL in the browser. To prevent sensitive information from users, the attackers/hackers make duplicate websites and send them to victims through phishing emails. In this article, the machine learning framework is used to find malicious URLs. Here, five different machine learning algorithms such as the logistic regression algorithm, K-nearest neighbor algorithm, decision tree algorithm, random forest algorithm, and support vector machine algorithm have been used. An ensemble modeling has been done using these algorithms, and the performance of each algorithm has been compared. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.