Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI

Marshall Elam, Dustin Mink, Sikha S. Bagui (), Russell Plenkers and Subhash C. Bagui
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Marshall Elam: Department of Computer Science, The University of West Florida, Pensacola, FL 32514, USA
Dustin Mink: Department of Cybersecurity, The University of West Florida, Pensacola, FL 32514, USA
Sikha S. Bagui: Department of Computer Science, The University of West Florida, Pensacola, FL 32514, USA
Russell Plenkers: Department of Computer Science, The University of West Florida, Pensacola, FL 32514, USA
Subhash C. Bagui: Department of Mathematics and Statistics, The University of West Florida, Pensacola, FL 32514, USA

Data, 2025, vol. 10, issue 5, 1-28

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.

Keywords: cybersecurity; network traffic; Enterprise MITRE ATT&CK Framework; labeled dataset; machine learning; AI; network security (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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