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UWF Datasets

UWF Datasets

The complete set of files are in PCAP and parquet formats and available at: https://datasets.uwf.edu/data/. This dataset consists of Zeek data files labelled using the MITRE ATT&CK Framework. The files in csv format are a subset of the files in parquet format, mainly made available for people who do not have access to "Big Data" technologies.

Data Card

Details about the CSV files:

Due to the fact that Excel's display limit is 1 million rows, the CSV file has only 1 million rows of data. Other details:

This document contains:

  1. Zeek Files and File Descriptions
  2. Attributes in Zeek Files
  3. Number of Records in Each File
  4. Distribution of Malicious Traffic in UWF-Zeekdata22
  5. MITRE ATT&CK Techniques in UWF-ZeekData22
  6. MITRE ATT&CK Tactics in UWF-ZeekData22 Dataset
  7. Un-flattened Tactics Count
  8. Individual File Descriptions

Authors

Members of this Cyber Analytics Research Group (CAR) (past and present):

  1. Faculty: Dr. Sikha Bagui at bagui@uwf.edu, Dr. Dustin Mink at dmink@uwf.edu, and Dr. Subhash Bagui at sbagui@uwf.edu
  2. Doctoral Research Assistants: Marshall Elam
  3. Graduate Research Assistants: Emily Miller, Andrew Palmer, Beri Peric, Anthony Simpson, and Thomas Thibaut
  4. Undergraduate Research Assistants: Mohammed Alquraishi, Stephan Dulaney, Molly Ferguson, Max Fina, Jadarius Hill, Jiya Huang, Nitisha Khanavis, Pooja Madhalya, Farooq Mahmud. Tom McElroy, Esteban Paredes, Michael Plain, Ricky Salinas, Sajida Shabanali, Sakthi Subramaniam, Emily Summers, Neha Uppal, and Daniel Wallace

Cite and Share

  1. Miller, E.; Mink, D.; Spellings, P.; Bagui, S.S.; Bagui, S.C. Classifying Cyber Ranges: A Case-Based Analysis Using the UWF Cyber Range. Encyclopedia 2025, 5, 162. https://doi.org/10.3390/encyclopedia5040162

  2. Bagui, S.S.; Eller, C.; Armour, R.; Singh, S.; Bagui, S.C.; Mink, D. Analyzing Performance of Data Preprocessing Techniques on CPUs vs. GPUs with and Without the MapReduce Environment. Electronics 2025, 14, 3597. https://doi.org/10.3390/electronics14183597 (Feature Paper)

  3. Bagui, S.S.; Khan, M.P.; Valmyr, C.; Bagui, S.C.; Mink, D. Model Retraining upon Concept Drift Detection in Network Traffic Big Data. Future Internet 2025, 17, 328. https://doi.org/10.3390/fi17080328

  4. Bagui, S.S.; Carvalho, G.C.S.D.; Mishra, A.; Mink, D.; Bagui, S.C.; Eager, S. Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment. Future Internet 2025, 17, 267. https://doi.org/10.3390/fi17060267

  5. Elam, M.; Mink, D.; Bagui, S.S.; Plenkers, R.; Bagui, S.C. Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI. Data 2025, 10, 59. https://doi.org/10.3390/data10050059

  6. Krebs, R.; Bagui, S.S.; Mink, D.; Bagui, S.C. Applying Multi-CLASS Support Vector Machines: One-vs.-One vs. One-vs.-All on the UWF-ZeekDataFall22 Dataset. Electronics 2024, 13, 3916. https://doi.org/10.3390/electronics13193916 (Feature Paper)

  7. Charkhabi, S.; Samimi, P.; Bagui, S.S.; Mink, D.; Bagui, S.C. Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph. Computers 2024, 13, 171. https://doi.org/10.3390/computers13070171

  8. Moomtaheen, F.; Bagui, S.S.; Bagui, S.C.; Mink, D. Extended Isolation Forest for Intrusion Detection in Zeek Data. Information 2024, 15, 404. https://doi.org/10.3390/info15070404

  9. Bagui, S.S.; Mink, D.; Bagui, S.C.; Subramaniam, S. Resampling to Classify Rare Attack Tactics in UWF-ZeekData22. Knowledge 2024, 4, 96-119. https://doi.org/10.3390/knowledge4010006

  10. Bagui, S.S.; Mink, D.; Bagui, S.C.; Sung, D.H.; Mahmud, F. Graphical Representation of UWF-ZeekData22 Using Memgraph. Electronics 2024, 13, 1015. https://doi.org/10.3390/electronics13061015

  11. Bagui, S.S.; Mink, D.; Bagui, S.C.; Madhyala, P.; Uppal, N.; McElroy, T.; Plenkers, R.; Elam, M.; Prayaga, S. Introducing the UWF-ZeekDataFall22 Dataset to Classify Attack Tactics from Zeek Conn Logs Using Spark's Machine Learning in a Big Data Framework. Electronics 2023, 12, 5039. https://doi.org/10.3390/electronics12245039

  12. Bagui, S.S.; Mink, D.; Bagui, S.C.; Subramaniam, S. Determining Resampling Ratios Using BSMOTE and SVM-SMOTE for Identifying Rare Attacks in Imbalanced Cybersecurity Data. Computers 2023, 12, 204. https://doi.org/10.3390/computers12100204 (Editor's Choice)

  13. Bagui, S.S.; Mink, D.; Bagui, S.C.; Plain, M.; Hill, J.; Elam, M. Using a Graph Engine to Visualize the Reconnaissance Tactic of the MITRE ATT&CK Framework from UWF-ZeekData22. Future Internet 2023, 15, 236. https://doi.org/10.3390/fi15070236

  14. Bagui, S.; Mink, D.; Bagui, S.; Subramaniam, S.; Wallace, D. Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks. Future Internet 2023, 15, 130. https://doi.org/10.3390/fi15040130

  15. Bagui, S.S.; Mink, D.; Bagui, S.C.; Ghosh, T.; Plenkers, R.; McElroy, T.; Dulaney, S.; Shabanali, S. Introducing UWF-ZeekData22: A Comprehensive Network Traffic Dataset Based on the MITRE ATT&CK Framework. Data 2023, 8, 18. https://doi.org/10.3390/data8010018 (Editor's Choice)

  16. Bagui, S.; Mink, D.; Bagui, S.; Ghosh, T.; McElroy, T.; Paredes, E.; Khasnavis, N.; Plenkers, R. Detecting Reconnaissance and Discovery Tactics from the MITRE ATT&CK Framework in Zeek Conn Logs Using Spark's Machine Learning in the Big Data Framework. Sensors 2022, 22, 7999. https://doi.org/10.3390/s22207999

Licensing Information

These articles and datasets are open access article and datasets distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).