Optimizing malicious website prediction: An advanced XGBoost-based machine learning model

Abstract In the substantial area of the Internet, some websites can be quite harmful and troublesome for both individuals and businesses. Our methods for identifying and forecasting these malicious websites are not always reliable; they can be slow and inaccurate. What if you had technology that could alert you to websites that pose a risk before issues arise? We are attempting to make that a reality. Despite the availability of online resources and additional research, there is a gap in our understanding. The existing approaches are not always the greatest; thus, we need a clear and consistent technique to use smart computers to forecast malicious websites. This makes it difficult for others attempting comparable tasks to compare our findings with theirs. Therefore, the study aims to close this disparity. With the help of an intelligent technology known as the XGBoost classifier and a set of data from the data-rich website Kaggle, we have devised a comprehensive strategy to address the issues identified with the existing methods for identifying and predicting malicious websites. The main objective of this study is to improve upon the current methods for identifying and predicting malicious websites. Our methodology includes data collection, data cleaning, and the application of the XGBoost classifier. To ensure accuracy, we validated our findings with thorough performance evaluations. Our approach achieved an impressive accuracy score of 95.5%, significantly outperforming previous methods. This study not only demonstrates the effectiveness of the XGBoost approach but also provides guidance for other researchers looking to identify and predict malicious websites more accurately, contributing to a safer internet environment.