The Internet is used by billions of users every day because it offers fast and free communication tools and platforms. Nevertheless, with this significant increase in usage, huge amounts of spam are generated every second, which wastes internet resources and, more importantly, users' time. This study investigates the use of machine learning models to classify URLs as spam or nonspam. 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. Experimental results demonstrate that bagging outperformed other models and achieved the highest accuracy of 98.64%. In addition, bagging outperformed the current state-of-the-art approaches which emphasize its effectiveness in addressing spam-related challenges on the Internet. This suggests that bagging is a promising approach for URL spam classification.

翻译：互联网每日被数十亿用户使用，因其提供快速且免费的通信工具与平台。然而，随着使用量的显著增长，每秒产生的海量垃圾信息不仅浪费网络资源，更严重消耗用户时间。本研究探究运用机器学习模型对URL进行垃圾与非垃圾分类。我们首先从仅含单一特征的URL中提取特征，随后比较了K近邻、装袋法、随机森林、逻辑回归等多种模型的性能。实验结果表明，装袋法以98.64%的最高准确率超越其他模型，且优于当前最前沿的方法，凸显了其在解决互联网垃圾相关问题中的有效性。这表明装袋法是实现URL垃圾分类的一种极具前景的途径。