Shared keystroke dataset for continuous authentication
Shared keystroke dataset for continuous authentication
复制标题
用于持续身份验证的共享击键数据集
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
S. Upadhyaya
中科院分区：
文献类型：
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作者：
Yan Lindsay Sun;Hayreddin Çeker;S. Upadhyaya
击键动力学是计算机终端用户身份认证的一种有效的行为生物识别技术。使用击键动力学的连续或主动身份验证引起了研究人员的极大兴趣。然而，与其他生物识别模式相比，只有少数公共数据集可供研究界使用，主要是因为大规模数据收集的困难。即使是现有的，也普遍存在主体数量少、特征不广泛的问题。在本文中，我们提供了一个用于研究击键动力学的共享数据集的收集细节。我们收集了157名受试者的原始击键数据，允许他们转录固定文本并自由回答问题。该数据集的特征反映了打字模式的时间变化和不同键盘布局引起的扰动。为了展示我们的数据集的可用性和质量，我们应用了一个现有的算法，即高斯混合模型对数据集进行击键分析并报告结果。
Keystroke dynamics is an effective behavioral biometrics for user authentication at a computer terminal. Continuous or active authentication using keystroke dynamics has raised a lot of interest among researchers. However, there are only a few public datasets available for the research community compared to other biometric modalities primarily because of the difficulty of large scale data collection. Even the existing ones generally suffer from small number of subjects and lack of extensive features. In this paper, we provide the details on the collection of a shared dataset for the study of keystroke dynamics. We have collected raw keystroke data from 157 subjects allowing them to transcribe fixed text and answer questions freely. The dataset is characterized to reflect the temporal variations of typing patterns and the perturbations caused by different keyboard layouts. To show the usability and the quality of our dataset, we apply an existing algorithm, viz. Gaussian mixture model for keystroke analysis on the dataset and report the results.