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Updated: Feb 6, 2026

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防范恶意偏见威胁 (GAMBiT) 数据集:揭示人类受试者的认知偏见红队网络范围操作
Brandon Beltz1, Jim Doty1, Yvonne Fonken2
1Bulls Run Group, 9207 Bulls Run Parkway, Bethesda, MD 20817-2403, USA.
Data in brief
|February 5, 2026
概括
三个红色团队在网络范围内的黑客实验的数据集被发布. 这些数据支持对攻击者行为建模和偏见感知分析的研究,以改善网络安全.
科学领域:
- 网络安全 网络安全
- 人与计算机的交互
- 行为科学 行为科学
背景情况:
- 网络范围环境对于现实的网络安全培训和研究至关重要.
- 了解人类攻击者的行为对于开发有效的防御策略至关重要.
- 防范恶意偏差威胁 (GAMBiT) 项目旨在解决网络安全分析中的偏差.
研究的目的:
- 从大规模的人体受试者网络安全实验中发布全面的数据集.
- 为建模攻击者行为和开发偏见意识分析提供数据.
- 为了使网络安全研究方法的基准测试.
主要方法:
- 进行了三次实验,每次有19-20名熟练的攻击者,每天8小时.
- 利用模拟企业网络 (SimSpace网络力量平台) 进行现实的操作.
- 收集多模式数据,包括自我报告,操作笔记,终端历史记录,密钥日志,PCAP和NIDS警报.
主要成果:
- 从熟练的攻击者执行现实的网络行动中收集了广泛的多模式数据.
- 包括衍生品策划日志和标签,以提高研究实用性.
- 在受控约束下确保数据收集,并使用标准化的起始环境.
结论:
- 发布的数据集为推进网络安全研究提供了宝贵的资源.
- 促进研究人类攻击者的行为和开发更强大的AI防御.
- 能够对网络安全研究方法进行比较分析和验证.
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