(untitled) (International Journal of Food and Nutritional Sciences, 2022)
Throughout the years, internet usage has grown significantly. The internet continues to transform how we interact with people, organise the flow of goods, and communicate knowledge all across the world. The attackers have used this popularity to their advantage to participate in illegal activities that would lead to monetary advantage. There has been a rise in malicious websites that launch client-side attacks over time, which cannot be identified effectively by existing approaches such as blacklisting. As a result, an efficient solution to detect these malicious websites is required. In this study, we used the random forest 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%