CLICKA: Collecting and leveraging identity cues with keystroke dynamics
CLICKA: Collecting and leveraging identity cues with keystroke dynamics
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DOI：
10.1016/j.cose.2022.102780
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发表时间：
2022-05
期刊：
影响因子：
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通讯作者：
Oliver Buckley;Duncan Hodges;John T. Windle;Sally Earl
中科院分区：
文献类型：
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作者：
Oliver Buckley;Duncan Hodges;John T. Windle;Sally Earl
IT 系统的保护方式通常是通过使用用户名和密码对。然而，这些凭证很容易丢失、被盗或被泄露。行为生物识别技术的使用可用于补充这些凭证，从而为经过身份验证的用户的身份提供更高级别的保证。然而，用户行为也可用于确定有关个人的其他可识别信息。在本文中，我们基于击键动力学（打字行为分析）的概念来推断匿名用户的姓名并预测他们的母语。这项工作发现，用户名称中包含的二元组（基于其时间）的排名与非用户名称中的二元组的排名存在明显差异。因此，我们建议个人以明显不同的方式可靠地输入他们熟悉的信息。在我们的研究中，我们发现，纯粹根据用户的输入方式（而不是输入内容），应该可以识别大约三分之一的形成匿名用户名的二元组。
The way in which IT systems are usually secured is through the use of username and password pairs. However, these credentials are all too easily lost, stolen or compromised. The use of behavioural biometrics can be used to supplement these credentials to provide a greater level of assurance in the identity of an authenticated user. However, user behaviours can also be used to ascertain other identifiable information about an individual. In this paper we build upon the notion of keystroke dynamics (the analysis of typing behaviours) to infer an anonymous user’s name and predict their native language. This work found that there is a discernible difference in the ranking of bigrams (based on their timing) contained within the name of a user and those that are not. As a result we propose that individuals will reliably type information they are familiar with in a discernibly different way. In our study we found that it should be possible to identify approximately a third of the bigrams forming an anonymous users name purely from how (not what) they type.