Abstract Personal keystroke modes are difficult to imitate and can therefore be used for identity authentication. The keystroke habits of a person can be learned according to the keystroke data generated when the person inputs free text. Detecting a user's keystroke habits as the user enters text can continuously verify the user's identity without affecting user input. The method proposed in this paper authenticates users via their keystrokes when they type free text. The user keystroke data is divided into a fixed-length keystroke sequence, which is then converted into a keystroke vector sequence according to the time feature of the keystroke. A model that combines a convolutional neural network and a recursive neural network is used to learn a sequence of individual keystroke vectors to obtain individual keystroke features for identity authentication. The model is tested using two open datasets, and the best false rejection rate (FRR) is found to be (2.07%,6.61%), the best false acceptance rate (FAR) is found to be (3.26%, 5.31%), and the best equal error rate (EER) is found to be (2.67%, 5.97%).

中文翻译：

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基于CNN和RNN的自由文本击键连续认证

摘要 个人击键方式难以模仿，可用于身份认证。根据人输入自由文本时产生的按键数据，可以了解人的按键习惯。在用户输入文本时检测用户的击键习惯可以在不影响用户输入的情况下不断验证用户的身份。本文提出的方法通过用户键入自由文本时的击键来验证用户。用户击键数据被划分为固定长度的击键序列，然后根据击键的时间特征转换为击键向量序列。使用卷积神经网络和递归神经网络相结合的模型来学习一系列单独的击键向量，以获得用于身份认证的个体击键特征。该模型使用两个开放数据集进行测试，发现最佳误拒绝率（FRR）为（2.07％，6.61％），发现最佳误接受率（FAR）为（3.26％，5.31％），发现最佳等错误率 (EER) 为 (2.67%, 5.97%)。