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

By Iman Sharafaldin, Arash Habibi Lashkari, Saqib Hakak, Ali Akbar Ghorbani. International Carnahan Conference on Security Technology, 2019

1,025 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d1aa9a31aea54c196ec32e94e1112a1c05d1fbb4

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In Artificial Intelligence

  1. Ernesto Damiani
  2. Zhibo Zhang
  3. Fatma Taher
  4. Chan Yeob Yeun
  5. Hussam Al Hamadi

In Computer Networks and Communications

  1. Ernesto Damiani
  2. Zhibo Zhang
  3. Fatma Taher
  4. Chan Yeob Yeun
  5. Hussam Al Hamadi

In Signal Processing

  1. Ernesto Damiani
  2. Zhibo Zhang
  3. Fatma Taher
  4. Chan Yeob Yeun
  5. Hussam Al Hamadi

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