Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2022 Sep 1;22(17):6627. doi: 10.3390/s22176627.
In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.
为了提高混合场景中基于按键动力学和鼠标动力学的用户认证准确性，并考虑到不同场景中用户操作变化会加重用户状态变化，从而难以模拟用户行为，我们提出了一种名为 SIURUA 的用户认证方法。SIURUA 使用与场景无关的特征和与用户相关的特征进行用户识别。首先，基于按键数据和鼠标移动数据提取特征。接下来，获取与场景相关性低的与场景无关的特征。最后，将与场景无关的特征与用户相关的特征融合，以确保特征的完整性。实验结果表明，所提出的方法在混合场景中提高了用户认证准确性，实验中获得了 84%的准确率。
