Defending against Distributed Denial of Service (DDoS) in the Internet of Things (IoT) computing environment is a challenging task. DDoS attacks are type of collective attack in which attackers work together to compromise internet security and services. The resource-constrained devices used in IoT deployments have made it even easier for an attacker to break, because of the vast number of vulnerable IoT devices with significant compute power. This paper proposed an ensemble machine learning (ML) model using the bagging technique to detect and prevent DDoS attacks in the IoT computing environment. We carried out an Machine Learning experiment and evaluated our proposed model with the most recent DDoS attacks (CICDoS2019) dataset. We use seven validation metrics (classification accuracy, precision rate, recall rate, f1-score, Matthews Correlation Coefficient, false negative rate and false positive rate) to evaluate the performance of the proposed model. The results obtained in our experiment shows an improved performance with an overall maximum classification accuracy of 99.75%, precision rate of 99.99%, recall rate of 99.76%, f1-score of 99.87%, Matthews Correlation Coefficient of 0.000000214, false negative rate of 0.24% and 4.42% false positive rate.