Shared Multi-Keyboard and Bilingual Datasets to Support Keystroke Dynamics Research
Shared Multi-Keyboard and Bilingual Datasets to Support Keystroke Dynamics Research
复制标题
共享多键盘和双语数据集以支持击键动力学研究
DOI：
10.1145/3508398.3511516
复制
发表时间：
2022
期刊：
影响因子：
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通讯作者：
Wright, Robert
中科院分区：
文献类型：
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作者：
Wahab, Ahmed Anu;Hou, Daqing;Banavar, Mahesh;Schuckers, Stephanie;Eaton, Kenneth;Baldwin, Jacob;Wright, Robert
击键动态已被证明是一种有前途的方法，用于基于用户的打字节奏的用户认证。多年来，它已经看到越来越多的应用，如防止交易欺诈，帐户接管和身份盗窃。然而，由于键盘动态的可变性质，用户的打字模式可能在不同键盘上或在不同键盘语言设置中变化，这可能影响系统准确性。换句话说，当用人体工程学键盘测试时，用使用机械键盘收集的数据建模的算法可以显著不同地执行。类似地，用一种语言收集的数据建模的算法在用另一种语言测试时可能会有显著的不同。因此，有必要研究多种键盘和多种语言对键盘动态性能的影响。这促使我们开发了两个自由文本动态数据集。第一个是多键盘数据集，包括四（4）个物理键盘-机械，人体工程学，薄膜和笔记本电脑键盘-第二个是英语和汉语的双语键盘数据集。在半受控设置中，使用非侵入性基于网络的键盘记录器从总共86名参与者收集数据。据我们所知，这是第一个多键盘和双语的数据集，以及数据收集软件，将公开提供用于研究目的。我们的数据集的有用性证明了两个国家的最先进的自由文本算法的性能进行评估。
Keystroke dynamics has been shown to be a promising method for user authentication based on a user's typing rhythms. Over the years, it has seen increasing applications such as in preventing transaction fraud, account takeovers, and identity theft. However, due to the variable nature of keystroke dynamics, a user's typing patterns may vary on a different keyboard or in a different keyboard language setting, which may affect the system accuracy. In other words, an algorithm modeled with data collected using a mechanical keyboard may perform significantly differently when tested with an ergonomic keyboard. Similarly, an algorithm modeled with data collected in one language may perform significantly differently when tested with another language. Hence, there is a need to study the impact of multiple keyboards and multiple languages on keystroke dynamics performance. This motivated us to develop two free-text keystroke dynamics datasets. The first is a multi-keyboard keystroke dataset comprising of four (4) physical keyboards - mechanical, ergonomic, membrane, and laptop keyboards - and the second is a bilingual keystroke dataset in both English and Chinese languages. Data were collected from a total of 86 participants using a non-intrusive web-based keylogger in a semi-controlled setting. To the best of our knowledge, these are the first multi-keyboard and bilingual keystroke datasets, as well as the data collection software, to be made publicly available for research purposes. The usefulness of our datasets was demonstrated by evaluating the performance of two state-of-the-art free-text algorithms.
超越言语：推广用于生物识别验证的 D 向量
DOI：
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发表时间：
2019
期刊：
AAAI Conference on Artificial Intelligence
影响因子：
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作者：
Jacob Baldwin;Ryan Burnham;Andrew Meyer;Robert Dora;Robert Wright
通讯作者：
Robert Wright
击键认证算法的基准测试
DOI：
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发表时间：
2017
期刊：
International Workshop on Information Forensics and Security
影响因子：
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作者：
Jiaju Huang;Daqing Hou;S. Schuckers;Timothy Law;Adam Sherwin
通讯作者：
Adam Sherwin
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：
International Workshop on Information Forensics and Security
影响因子：
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作者：
Yan Lindsay Sun;Hayreddin Çeker;S. Upadhyaya
通讯作者：
S. Upadhyaya