A malicious attempt to exhaust a victim's resources to cause it to crash or halt its services is known as a distributed denial-of-service (DDoS) attack. DDOS attacks stop authorized users from accessing specific services available on the Internet. It targets varying components of a network layer and it is better to stop into layer 4 (transport layer) of the network before approaching a higher layer. This study uses several machine learning and statistical models to detect DDoS attacks from traces of traffic flow and suggests a method to prevent DDOS attacks. For this purpose, we used logistic regression, CNN, XGBoost, naive Bayes, AdaBoostClassifier, KNN, and random forest ML algorithms. In addition, data preprocessing was performed using three methods to identify the most relevant features. This paper explores the issue of improving the DDOS attack detection accuracy using the latest dataset named CICDDoS2019, which has over 50 million records. Because we employed an extensive dataset for this investigation, our findings are trustworthy and practical. Our target class (attack class) was imbalanced. Therefore, we used two techniques to deal with imbalanced data in machine learning. The XGboost machine learning model provided the best detection accuracy of (99.9999%) after applying the SMOTE approach to the target class, outperforming recently developed DDoS detection systems. To the best of our knowledge, no other research has worked on the most recent dataset with over 50 million records, addresses the statistical technique to select the most significant feature, has this high accuracy, and suggests ways to avoid DDOS attackI.