Keystroke-based behavioral biometrics have been proven effective for continuous user authentication. Current state-of-the-art algorithms have achieved outstanding results in long text or short text collected by doing some tasks. It remains a considerable challenge to authenticate users continuously and accurately with short keystroke inputs collected in uncontrolled settings. In this work, we propose a Timely Keystroke-based method for Continuous user Authentication, named TKCA. It integrates the key name and two kinds of timing features through an embedding mechanism. And it captures the relationship between context keystrokes by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. We conduct a series of experiments to validate it on a public dataset - the Clarkson II dataset collected in a completely uncontrolled and natural setting. Experiment results show that the proposed TKCA achieves state-of-the-art performance with 8.28% of EER when using only 30 keystrokes and 2.78% of EER when using 190 keystrokes.

事实证明，基于击键的行为生物特征识别对于连续的用户身份验证是有效的。当前的最新算法通过执行某些任务而在长文本或短文本中都取得了出色的结果。通过不受控制的设置中收集的短按键输入来连续准确地对用户进行身份验证仍然是一个巨大的挑战。在这项工作中，我们提出了一种基于时间击键的连续用户身份验证方法，称为TKCA。它通过嵌入机制集成了密钥名称和两种计时功能。并且它捕获了双向长期短期记忆（Bi-LSTM）网络的上下文击键之间的关系。我们进行了一系列实验，以在公共数据集上进行验证-在完全不受控制的自然环境中收集的Clarkson II数据集。