The Internet of Things (IoT) is a fast-developing technological domain that has seen remarkable expansion in recent years; however, the security of these devices is critical, particularly with Denial of Service (DoS) attacks, which seek to overload a service or device by using a machine to render the device inactive. In this sense, we propose two machine learning approaches: a Random Forest approach, which has an F1 score of 0.99985 and an inference time of 0.457026 s for almost 500,000 records, and another from XGBoost, with an F1 score of 0.998989 and an inference time of 0.325767 s for the same 500,000 records. According to the data set, the methodologies used, and their results, these models were the most suitable for addressing the security issues imposed by DoS attacks.