CIC-DDoS2019 Dataset

Distributed Denial of Service (DDoS) attack is a menace to network security that aims at exhausting the target networks with malicious traffic. 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. In this paper, first, we review the existing datasets comprehensively and propose a new taxonomy for DDoS attacks. Secondly, we generate a new dataset, namely CICDDoS2019, which remedies all current shortcomings. Thirdly, using the generated dataset, we propose a new detection and family classification approach based on a set of network flow features. Finally, we provide the most important feature sets to detect different types of DDoS attacks with their corresponding weights.

The dataset offers an extended set of Distributed Denial of Service attacks, most of which employ some form of amplification through reflection. The dataset shares its feature set with the other CIC NIDS datasets, IDS2017, IDS2018 and DoS2017

original paper link: https://ieeexplore.ieee.org/abstract/document/8888419
kaggle dataset link: https://www.kaggle.com/datasets/dhoogla/cicddos2019