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Journal article
Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI
Data (Basel), Vol.10(5), p.59
04/25/2025
Web of Science ID: WOS:001496667400001
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.
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Details
- Title
- Introducing UWF-ZeekData24
- Publication Details
- Data (Basel), Vol.10(5), p.59
- Resource Type
- Journal article
- Publisher
- MDPI
- Number of pages
- 28
- Grant note
- 2021 NCAE-C-002: Cyber Research Innovation Grant Program: 2021 NCAE-C-002 Cyber Research Innovation Grant Program: H98230-21-1-0170 Askew Institute at the University of West Florida
This research was funded by 2021 NCAE-C-002: Cyber Research Innovation Grant Program, Grant Number: H98230-21-1-0170. This research was also partially supported the Askew Institute at the University of West Florida.
- Copyright
- © the author(s)
- Identifiers
- WOS:001496667400001; 99381356538506600
- Academic Unit
- Mathematics and Statistics; Computer Science; Hal Marcus College of Science and Engineering
- Language
- English