Continuous authentication systems leveraging free-text keyboard dynamics offer a promising additional layer of security in a multifactor authentication setup that can be used in a transparent way with no impact on user experience. This study investigates the efficacy of behavioral biometrics by employing an Agent-Based Model (ABM) to simulate diverse typing profiles across mechanical and membrane keyboards. Specifically, we generated synthetic keystroke data from five unique agents, capturing features related to dwell time, flight time, and error rates within sliding 5-second windows updated every second. Two machine learning approaches, One-Class Support Vector Machine (OC-SVM) and Random Forest (RF), were evaluated for user verification. Results revealed a stark contrast in performance: while One-Class SVM failed to differentiate individual users within each group, Random Forest achieved robust intra-keyboard user recognition (Accuracy > 0.7) but struggled to generalize across keyboards for the same user, highlighting the significant impact of keyboard hardware on typing behavior. These findings suggest that: (1) keyboard-specific user profiles may be necessary for reliable authentication, and (2) ensemble methods like RF outperform One-Class SVM in capturing fine-grained user-specific patterns.

本研究解决了当前连续认证系统中存在的用户身份验证差异问题，采用代理建模（ABM）模拟了不同机械键盘和膜键盘的打字特征。研究发现，虽然随机森林算法在同一键盘上的用户识别表现良好，但在不同键盘间的泛化能力不足，因此需要为可靠认证建立特定于键盘的用户配置文件。

基于代理建模的自由文本键盘动态在连续认证中的应用