Unified Host and Network Data Set
Not on the map. Why?
Abstract (arXiv)
The lack of data sets derived from operational enterprise networks continues to be a critical deficiency in the cyber security research community. Unfortunately, releasing viable data sets to the larger com- munity is challenging for a number of reasons, primarily the difficulty of balancing security and privacy concerns against the fidelity and utility of the data. This chapter discusses the importance of cyber secu- rity research data sets and introduces a large data set derived from the operational network environment at Los Alamos National Laboratory. The hope is that this data set and associated discussion will act as a catalyst for both new research in cyber security as well as motivation for other organizations to release similar data sets to the community.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Aug 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 3; 2023: 0; 2024: 0; 2025: 2; 2026: 3.
11 papers.
- 2022Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learningcited by 3
- 2022Local2Global: A distributed approach for scaling representation learning on graphscited by 2
- 2022Deception for Cyber Defence: Challenges and Opportunitiescited by 2
- 2021Learning to Detect: A Data-driven Approach for Network Intrusion Detectioncited by 1
- 2025Contextual Learning for Anomaly Detection in Tabular Datacited by 1
- 2021Secure solutions for Smart City Command Control Centre using AIOTcited by 0
- 2021DeepTaskAPT: Insider APT detection using Task-tree based Deep Learningcited by 0
- 2025A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detectioncited by 0
- 2026TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulationcited by 0
- 2026Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluationcited by 0
- 2026Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detectioncited by 0
- 2026Hybrid Latent-Structural Fusion (HLSF) for Cyber Anomaly Detection20 Jul 2026
- 2026Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation16 Jun 2026
- 2026TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation22 Jan 2026
- 2025A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection30 Oct 2025
- 2025Contextual Learning for Anomaly Detection in Tabular Data10 Sep 2025
- 2022Deception for Cyber Defence: Challenges and Opportunities15 Aug 2022
- 2022Dynamic Network Reconfiguration for Entropy Maximization using Deep Reinforcement Learning26 May 2022
- 2022Local2Global: A distributed approach for scaling representation learning on graphs12 Jan 2022
- 2021DeepTaskAPT: Insider APT detection using Task-tree based Deep Learning31 Aug 2021
- 2021Learning to Detect: A Data-driven Approach for Network Intrusion Detection18 Aug 2021
- 2021Secure solutions for Smart City Command Control Centre using AIOT29 Jul 2021