Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights. Thirdly, using the generated dataset, we propose a new detection and family classification approach based on a set of network flow features. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. In this paper, we first review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Although many statistical methods have been designed for DDoS attack detection, designing a real-time detector with low computational overhead is still one of the main concerns. On the other hand, the evaluation of new detection algorithms and techniques heavily relies on the existence of well-designed datasets. Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic.
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