Cited by 1,275Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingNetwork Packet Processing and Optimization

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Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy

Sharafaldin, Iman, Lashkari, Arash Habibi, Hakak, Saqib, Ghorbani, Ali A.·2019·2019 International Carnahan Conference on Security Technology (ICCST)

DOI 10.1109/ccst.2019.8888419

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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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Authors: Sharafaldin, I., Lashkari, A. H., Hakak, S., & Ghorbani, A. A.
Affiliations: University of New Brunswick (CA)
ORCID: Sharafaldin, Iman
Lashkari, Arash Habibi
Hakak, Saqib
Ghorbani, Ali A.
Venue: 2019 International Carnahan Conference on Security Technology (ICCST)
Publisher: IEEE
Published: 2019-10
Pages: 1-8
DOI: 10.1109/ccst.2019.8888419
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: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
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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Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy

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