In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.

中文翻译：

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基于MKL的按键和鼠标行为特征融合用户认证方法

为了提高具有单一行为特征的用户的识别率并防止冒名顶替者限制输入设备来避免检测，提出了一种基于多核学习（MKL）的双键用户认证方法，用于按键和鼠标行为特征融合。这张纸。由于按键特征和鼠标特征之间的异质性，我们认为将每种类型的特征都映射到合适的内核，并且通过计算获得每个内核的权重，然后求和以获得实现多特征融合的复合内核。本文使用的数据集是在完全不受控制的条件下使用我们的数据收集程序从一些志愿者那里收集的。实验结果表明，该方法可以达到89.6％的最佳识别精度。与传统的单特征方法相比，双索引方法可以获得更稳定，更有效的认证。因此，本文提出的方法充分证明了双索引用户认证的可靠性。