SDN-DDoS Traffic Dataset

Published: 8 May 2024| Version 1 | DOI: 10.17632/b7vw628825.1


In cybersecurity, understanding and mitigating Distributed Denial of Service (DDoS) attacks are paramount. Although public datasets offer valuable insights, they often lack the specific characteristics or scale necessary for comprehensive research. Hence, the generation of tailored datasets is imperative for addressing the limitations of public resources. This dataset serves as a crucial resource for evaluating and refining AI algorithms, particularly in the domains of machine and deep learning. By providing a diverse array of DDoS attack traffic scenarios, researchers can develop and validate models capable of detecting and mitigating such threats in real-time. Furthermore, the dataset encompasses two distinct network topologies, each comprising 12 switches and 24 hosts, orchestrated by a Ryu controller in a Software-Defined Networking (SDN) environment. This setup enables the simulation of complex network behaviors and generation of realistic traffic patterns that are reflective of actual deployment scenarios. The dataset includes a comprehensive set of features that are essential for characterizing network traffic dynamics during DDoS attacks. These features encompass critical parameters, such as source and destination IP addresses, packet and byte counts, duration metrics, flow characteristics, protocol details, port numbers, transmission rates, delay, jitter, packet loss rates, and labeled annotations. In total, our dataset comprised an extensive collection of 1,048,575 rows, ensuring a robust and diverse sample size for rigorous analysis and experimentation. This scale facilitates the exploration of nuanced patterns and behaviors across various attack scenarios and network configurations. Furthermore, our dataset adheres to ethical guidelines and privacy standards, ensuring the anonymization of sensitive information and protection of individual privacy rights. Researchers can leverage this dataset with confidence, knowing that it upholds ethical principles while advancing the state-of-the-art DDoS detection and mitigation strategies.


Steps to reproduce

Ethical Considerations:  The dataset is intended for research purposes only and should not be used for any malicious activities or unauthorized access to networks.  Researchers are encouraged to handle the data responsibly and ethically, respecting privacy rights and confidentiality agreements. Citation Information: If you use our dataset in your research, please cite it using the following format: Hirsi, Abdinasir; Audah, Lukman; Salh, Adeb (2024), “SDN-DDoS Traffic Dataset”, Mendeley Data, V1, doi: 10.17632/b7vw628825.1


Universiti Tun Hussein Onn Malaysia