URL Security Assessment Using Machine Learning

The significance of the World Wide Web has expanded considerably over time. However, alongside technological progress, there has been a rise in the complexity of methods aimed at exploiting users. These efforts often involve infecting users' computers with malware or directing them to unfriendly websites for purposes such as peddling counterfeit goods or exposing sensitive information, leading to financial fraud. Malicious URLs pose a significant threat to potential victims by hosting a range of unwanted content. Therefore, there is a pressing need for a rapid and effective detection approach. This thesis addresses the challenge of identifying hazardous URLs by leveraging URL data and machine learning techniques. Various machine learning algorithms, including XGBoost, LightGBM, and Random Forest, will be utilized for this purpose. Key Words: Malicious URLs, XGBoost, LightGBM, Random Forest