Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI
Year: 2025
Type: article
Abstract: This paper describes the creation of a new dataset, UWF-ZeekData24, aligned with the Enterprise MITRE ATT&CK Framework, that addresses critical shortcomings in existing network security datasets. Controlling the construction of attacks and meticulously labeling the data provides a more accurate and dynamic environment for testing of IDS/IPS systems and their machine learning algorithms. The outcomes of this research will assist in the development of cybersecurity solutions as well as increase the robustness and adaptability towards modern day cybersecurity threats. This new carefully engineered dataset will enhance cyber defense mechanisms that are responsible for safeguarding critical infrastructures and digital assets. Finally, this paper discusses the differences between crowd-sourced data and data collected in a more controlled environment.
Source: Data
DOI: 10.3390/data10050059
Authors: Marshall Elam, Dustin Mink, Sikha S. Bagui, Sikha S. Bagui, Russell Plenkers, Subhash C. Bagui, Subhash C. Bagui
Institution: University of West Florida
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W4409805523