Malicious URL Detection Using Machine Learning (International Journal of Food and Nutritional Sciences, 2022)
Throughout the years, internet usage has grown significantly. The internet continues totransform how we interact with people, organise the flow of goods, and communicateknowledge all across the world. The attackers have used this popularity to their advantageto participate in illegal activities that would lead to monetary advantage. There has been arise in malicious websites that launch client-side attacks over time, which cannot beidentified effectively by existing approaches such as blacklisting. As a result, an efficientsolution to detect these malicious websites is required. In this study, we used the randomforest method to develop a machine learning model while integrating lexical features, hostbased features, and content-based features. The model has an accuracy of 94.7%