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Updated: Jul 5, 2025

A Cross-Disciplinary and Multi-Modal Experimental Design for Studying Near-Real-Time Authentic Examination Experiences
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性能计数器数据集用于行为生物识别目的.

Cesar Andrade1, Hendrio Bragança1, Eduardo Feitosa1

  • 1Institute of Computing, Federal University of Amazonas, Amazonas, Brazil.

Data in brief
|January 16, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍了两个新的Windows性能计数器数据集,COUNT-OS-I和COUNT-OS-II,以推进持续用户身份验证研究. 这些数据集提供真实世界的数据,用于开发强大的身份验证模型和增强系统安全性.

关键词:
行为生物识别技术持续的身份验证验证机器学习是机器学习.性能计数器是指性能计数器.

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科学领域:

  • 计算机科学 计算机科学
  • 网络安全 网络安全
  • 数据科学数据科学数据科学

背景情况:

  • 持续的用户身份验证对于现代网络安全至关重要.
  • 现有的数据集可能缺乏现实世界的多样性或全面的指标.
  • 推进认证模型需要高质量,具有代表性的数据.

研究的目的:

  • 介绍COUNT-OS-I和COUNT-OS-II,这是Windows性能计数器的新型数据集.
  • 提供丰富的现实数据,用于开发和评估持续用户身份验证模型.
  • 建立可靠的基准来促进用户身份验证方面的创新.

主要方法:

  • 收集了63台Windows计算机和用户的性能计数器数据.
  • COUNT-OS-I:26个用户,不同的环境,159个属性,~26小时/用户.
  • COUNT-OS-II:37个用户,相同的配置,218个属性,48小时.
  • 采用伪名化来保护用户隐私.

主要成果:

  • 数据集提供了全面的,统计验证的绩效指标.
  • COUNT-OS-I和COUNT-OS-II可以捕捉到不同的用户和系统交互.
  • 数据是平衡的,适合严格的模型训练和测试.

结论:

  • COUNT-OS-I和COUNT-OS-II是持续用户身份验证研究的宝贵资源.
  • 这些数据集将有助于开发更强大,更可靠的身份验证系统.
  • 此次发布旨在加快在保护用户访问和增强数字信任方面的进展.