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Body-Taps: Authenticating Your Device Through Few Simple Taps

机译:轻按:通过几次简单的轻按即可验证设备

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To fulfill the increasing demands on authentication methods on the smart mobile and wearable devices with small form factors and constrained screen displays, we introduce a novel authentication mechanism, Body-Taps, which authenticates a device based on the Tap-Code gestures in the form of hand movements captured through the built-in motion sensors. The Body-Taps require a user to set a TapCode as an unlock code for the device by tapping the device on the set anchor points on his or her own body. The target device is authenticated based on two criterion: (1) the user's knowledge of the set Tap-Code, and (2) the BodyTap gestures measured through the smart device's built-in motion sensors (accelerometer and gyroscope). Our experiments show that the proposed Body-Taps system can achieve an average authentication accuracy over 99.5% on a dataset comprising of 230 Body-Tap samples from 23 subjects, using Random Forest (RF), Neural Network (NNet), and Linear Discriminant Analysis (LDA) classifiers. Our work yields a light-weight, low-cost, and easy-to-use secure authentication system that requires minimal efforts and offers satisfactory usability.
机译:为了满足对具有小尺寸和受限屏幕显示的智能移动和可穿戴设备上身份验证方法不断增长的需求,我们引入了一种新颖的身份验证机制,即Body-Taps,它可以根据Tap Code手势以以下形式对设备进行身份验证:通过内置的运动传感器捕获手部动作。身体轻拍要求用户通过在他或她自己的身体上设置的锚点上轻按设备来将TapCode设置为设备的解锁代码。基于两个标准对目标设备进行身份验证:(1)用户对设置的Tap Code的了解,以及(2)通过智能设备的内置运动传感器(加速度计和陀螺仪)测量的BodyTap手势。我们的实验表明,使用随机森林(RF),神经网络(NNet)和线性判别分析,所提出的Body-Taps系统在包含来自23个受​​试者的230个Body-Tap样本的数据集上,可以达到平均99.5%的平均身份验证准确度(LDA)分类器。我们的工作产生了重量轻,成本低且易于使用的安全身份验证系统,该系统只需花费很少的精力即可提供令人满意的可用性。

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