Abstract

This paper investigates some specific features of continuous user identification based on hidden monitoring of keystroke dynamics when creating a free text. Our analysis of static identification approaches does not reveal any significant limitations on their application to continuous identification. The main feature of continuous identification is the method for collecting dynamic information about key presses and the correction of templates of registered users. The effectiveness of including additional classification features in recognition algorithms, e.g., those associated with the frequency of letters in texts, is demonstrated. A software application is developed to collect and analyze keystroke rhythm samples of users. Research in the domain of users with good computer skills shows quite satisfactory user recognition accuracy (87% on average). Moreover, the accuracy does not depend on the metric distance selected for recognition and improves with the use of scaling factors for letter frequency.

中文翻译：

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基于自由文本和隐藏监视的连续用户识别方面

摘要

本文研究了在创建自由文本时基于对按键动态的隐藏监视而实现的连续用户识别的某些特定功能。我们对静态识别方法的分析并未发现其在连续识别中的任何重大限制。连续识别的主要特征是用于收集有关按键动态信息和注册用户模板校正的方法。证明了在识别算法中包括其他分类功能的有效性，例如与文本中字母频率相关的那些功能。开发了软件应用程序以收集和分析用户的击键节奏样本。具有良好计算机技能的用户领域的研究显示出令人满意的用户识别准确性（平均87％）。