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相关实验视频

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An Assessment Method and Toolkit to Evaluate Keyboard Design on Smartphones
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KeyRecs:一个按键动态和打字模式识别数据集.

Tiago Dias1, João Vitorino1, Eva Maia1

  • 1Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), School of Engineering, Polytechnic of Porto (ISEP/IPP), 4249-015 Porto, Portugal.

Data in brief
|September 4, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了KeyRecs数据集,其中包括用于连续用户身份验证的键击动态. 这些数据增强了生物识别系统,以提高安全性和防止未经授权的访问.

关键词:
异常检测检测异常检测生物识别认证的真实性机器学习是机器学习.打字行为 打字行为

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科学领域:

  • 生物识别和人机交互的人机交互
  • 网络安全和身份验证技术

背景情况:

  • 按键动态为持续的用户身份验证提供了被动和非侵入性的方法.
  • 智能认证系统可以从分析打字模式中获益,以提高安全性.
  • 现有的数据集可能缺乏全面的打字行为和人口统计数据来进行强大的模型训练.

研究的目的:

  • 介绍和描述KeyRecs数据集,这是一个用于按键动态研究的新型资源.
  • 提供数据集,支持开发先进的生物识别身份验证系统.
  • 为了促进用户识别和防止未经授权的访问使用打字行为研究.

主要方法:

  • 收集了99名参与者从固定文本和自由文本打字练习中收集的按键数据 (关键间延迟).
  • 记录了Digraph延迟,测量了关键新闻和发布事件之间的时间.
  • 收集了每个参与者的人口统计信息,包括年龄,性别,手性和国籍.

主要成果:

  • KeyRecs数据集包括多种类型的打字样本和相关的人口数据.
  • 数据捕获了用户与键盘交互的细粒度时间信息.
  • 该数据集适合用于训练和评估用于用户身份验证的机器学习模型.

结论:

  • 在KeyRecs数据集中捕获的按键键动态对于智能身份验证非常有价值.
  • KeyRecs数据集可以显著提高授权用户识别的准确性.
  • 利用此数据集有助于开发更有效的生物识别身份验证软件,以防止未经授权的访问.