Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations
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arXiv
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| Format: | Preprint |
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2025
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| author | Beltz, Brandon Doty, Jim Fonken, Yvonne Gurney, Nikolos Israelsen, Brett Lau, Nathan Marsella, Stacy Thomas, Rachelle Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan |
| author_facet | Beltz, Brandon Doty, Jim Fonken, Yvonne Gurney, Nikolos Israelsen, Brett Lau, Nathan Marsella, Stacy Thomas, Rachelle Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan |
| contents | We present three large-scale human-subjects red-team cyber range datasets from the Guarding Against Malicious Biased Threats (GAMBiT) project. Across Experiments 1-3 (July 2024-March 2025), 19-20 skilled attackers per experiment conducted two 8-hour days of self-paced operations in a simulated enterprise network (SimSpace Cyber Force Platform) while we captured multi-modal data: self-reports (background, demographics, psychometrics), operational notes, terminal histories, keylogs, network packet captures (PCAP), and NIDS alerts (Suricata). Each participant began from a standardized Kali Linux VM and pursued realistic objectives (e.g., target discovery and data exfiltration) under controlled constraints. Derivative curated logs and labels are included. The combined release supports research on attacker behavior modeling, bias-aware analytics, and method benchmarking. Data are available via IEEE Dataport entries for Experiments 1-3. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20963 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations Beltz, Brandon Doty, Jim Fonken, Yvonne Gurney, Nikolos Israelsen, Brett Lau, Nathan Marsella, Stacy Thomas, Rachelle Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan Cryptography and Security Computer Science and Game Theory We present three large-scale human-subjects red-team cyber range datasets from the Guarding Against Malicious Biased Threats (GAMBiT) project. Across Experiments 1-3 (July 2024-March 2025), 19-20 skilled attackers per experiment conducted two 8-hour days of self-paced operations in a simulated enterprise network (SimSpace Cyber Force Platform) while we captured multi-modal data: self-reports (background, demographics, psychometrics), operational notes, terminal histories, keylogs, network packet captures (PCAP), and NIDS alerts (Suricata). Each participant began from a standardized Kali Linux VM and pursued realistic objectives (e.g., target discovery and data exfiltration) under controlled constraints. Derivative curated logs and labels are included. The combined release supports research on attacker behavior modeling, bias-aware analytics, and method benchmarking. Data are available via IEEE Dataport entries for Experiments 1-3. |
| title | Guarding Against Malicious Biased Threats (GAMBiT) Experiments: Revealing Cognitive Bias in Human-Subjects Red-Team Cyber Range Operations |
| topic | Cryptography and Security Computer Science and Game Theory |
| url | https://arxiv.org/abs/2508.20963 |