Distributed Denial of Service (DDoS) attacks disrupt global network services by mainly overwhelming the host victim with requests originating from multiple traffic sources. DDoS attacks are currently on the rise due to the ease of execution and rental of distributed architectures, which could potentially result in substantial revenue losses. Therefore, the detection and prevention of DDoS attacks are currently topics of high interest. In this study, we utilize traffic flow information to determine if a specific flow is associated with a DDoS attack. We evaluate traditional Machine Learning (ML) methods in developing our DDoS detector and utilize an exhaustive hyperparameter search to optimize the detection capability of each ML model. Our evaluation shows that most algorithms provide satisfactory results, with Random Forests achieving as high as 99% of detection accuracy, which is comparable to existing deep learning approaches.