Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy
Abstract
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 classificaiton 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.
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These deep sequence and generative models can be sensitive to noisy or irregular traffic, missing packets, and inaccurate labels, which may lead to degraded performance when real-world traffic deviates from the structured patterns seen during training.
“However, these models can be sensitive to noisy or irregular traffic, missing packets, and inaccurate labels, which may lead to degraded performance when real-world traffic deviates from the structured patterns seen during training [35,36,41].”
From AI-Based Anomaly Detection in Industrial Control and Cyber–Physical Systems: A Data-Type-Oriented Systematic Review (2025) · cites this paper as [41]
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- Authors
- Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A.
- Affiliations
- University of New Brunswick (CA)
- Venue
- 2019 International Carnahan Conference on Security Technology (ICCST)
- Publisher
- IEEE
- Published
- 2019-10
- Pages
- 1-8
- OpenAlex
- W2982682021
- Cited by
- 1,275
- References
- 17
- Citations by year
- 2026: 176 · 2025: 251 · 2024: 271 · 2023: 242 · 2022: 167 · 2021: 124 · 2020: 44
- FWCI
- 72.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Carnahan Conference on Security Technology (ICCST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- DDoS attacks, DDoS attack detection, CICDDoS2019, network flow features, feature weighting, real-time intrusion detection, malicious traffic
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