Hand in Motion: Enhanced Authentication Through Wrist and Mouse Movement
Hand in Motion: Enhanced Authentication Through Wrist and Mouse Movement
复制标题
DOI：
10.1109/btas.2018.8698577
复制
发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Borui Li;Wen Wang;Yang Gao;V. Phoha;Zhanpeng Jin
中科院分区：
文献类型：
--
作者：
Borui Li;Wen Wang;Yang Gao;V. Phoha;Zhanpeng Jin
长期以来，行为生物识别技术一直被用作传统一次性认证系统的补充方法。鼠标动态，代表了一个人的鼠标操作的独特模式，具有很大的潜力，以弥合计算机上的两个一次性认证之间的安全差距。在本文中，我们提出了一个连续的认证方法相结合的deviceindependent，基于角度的鼠标运动功能和手腕运动功能。基于随机森林包围盒分类器（RFEC）和序贯抽样分析（SSA），可以连续地验证用户的身份。基于26个被试的实验结果表明，该方法对冒名顶替者和入侵者的误接受率（FAR）分别为1.46%和4.69%，误查全率（FRR）为0%。此外，所提出的方法被证明在及时认证（即，仅在9到12次鼠标点击内做出认证决定），这与仅基于鼠标几何形状和运动特征的传统方法相比是非常重要的。
Behavioral biometrics have been long used as a complementary method to the traditional one-time authentication system. Mouse dynamics, representing an individual’s unique patterns of mouse operations, possess a great potential to bridge the security gap between two one-time authentications on the computer. In this paper, we propose a continuous authentication approach by combining the deviceindependent, angle-based mouse movement features and the wrist motion features. Based on a Random Forest Ensemble Classifier (RFEC) and the Sequential Sampling Analysis (SSA), the identity of the user can be continuously verified. Experimental results, based on 26 subjects, show that the proposed approach can reach the False Accept Rate (FAR) of 1.46% and 4.69% for impostors and intruders respectively and a False Reject Rate (FRR) of 0%. Moreover, the proposed approach is proven to be more effective in timely authentication (i.e., making an authentication decision within only 9 to 12 mouse clicks), compared with conventional methods solely based on the mouse geometry and locomotion features.