Guarding against malicious biased threats (GAMBiT) datasets: Revealing cognitive bias in human-subjects red-team cyber range operations

We present datasets from three large-scale human-subject experiments involving red-team hacking in a cyber range in 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 collecting multi-modal data: self-reports (background, demographics, psychometrics), operational notes, terminal histories, key logs, 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 data release supports research on attacker behavior modeling, bias-aware analytics, and method benchmarking. Data are available via IEEE DataPort entries for Experiments 1-3.