Classification of Spam URLs Using Machine Learning Approaches

Background:The Internet is used by billions of users every day because it offers fast and free communication tools and platforms. However, with this significant increase in usage, huge amounts of spam are generated every second, which wastes internet resources and, more importantly, users’ time. Objective:This study investigates the use of machine learning models to classify URLs as spam or non-spam. Methods: We first extract the features from the URL as it has only one feature, and then we compare the performance of several models, including k-nearest neighbors, bagging, random forest, logistic regression, and others. Results: Experimental results demonstrate that bagging outperformed other models, achieving the highest accuracy of 98.64%. In addition, bagging out- performed the current state-of-the-art approaches emphasize its effectiveness in addressing spam-related challenges on the Internet. Conclusion:This suggests that bagging is a promising approach for URL spam classification.