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.

翻译：在当今世界,互联网几乎到处都在使用。随着IoT技术的兴起(IoT技术是使用最多的技术之一),数十亿IoT设备在互联网上相互连接。然而,DOS/DDoS攻击是对这一不断增长的技术最经常和最危险的威胁。新的DDoS攻击类型是高度先进和复杂的,几乎不可能通过现有的入侵探测系统和传统方法探测或减轻。幸运的是,大数据、数据挖掘和机器学习技术使得能够有效检测DDoS的流量。本文建议了基于数据挖掘和机器学习技术的DDoS探测模型。为撰写本文,现有的最新数据集,CICDDoS-2019,以最受欢迎的机器学习算法进行了实验,并具体说明了与预测的类别最相关的特征。发现AdaBoost和XGBoost非常准确和正确地预测了100%的网络流量类型。未来研究可以通过加强不同DOS攻击类型多级的模型以及测试该模型的混合算法和新数据集来扩展。