A Distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt the normal traffic of a targeted server, service, or network by overwhelming it with a flood of requests from multiple compromised internet-connected devices, such as distributed servers, personal computers, and Internet of Things devices. One of the methods used to defend against DDoS attacks is traffic redirection to a Scrubbing Center (SC) for further inspection and mitigation. In this research, we present a novel scrubbing method that employs machine learning models to detect DDoS attacks. We propose using three machine learning algorithms, Random Forest, Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost), and combine them with three feature selection techniques, Analysis of Variance (ANOVA), Principal Component Analysis (PCA), and Kendall's Rank Correlation. Our results indicate that a combination of Kendall's Rank Correlation as a feature selector with SVM, XGBoost, and Random Forest models achieved a high F1 score.