  In the current world, the Internet is being used almost everywhere. With the
rise of IoT technology, which is one of the most used technologies, billions of
IoT devices are interconnected over the Internet. However, DoS/DDoS attacks are
the most frequent and perilous threat to this growing technology. New types of
DDoS attacks are highly advanced and complicated, and it is almost impossible
to detect or mitigate by the existing intrusion detection systems and
traditional methods. Fortunately, Big Data, Data mining, and Machine Learning
technologies make it possible to detect DDoS traffic effectively. This paper
suggests a DDoS detection model based on data mining and machine learning
techniques. For writing this paper, the latest available Dataset, CICDDoS2019,
experimented with the most popular machine learning algorithms and specified
the most correlated features with predicted classes are being used. It is
discovered that AdaBoost and XGBoost were extraordinarily accurate and
correctly predicted the type of network traffic with 100% accuracy. Future
research can be extended by enhancing the model for multiclassification of
different DDoS attack types and testing hybrid algorithms and newer datasets on
this model.
