论文标题
可穿戴设备用户的上下文依赖性隐式身份验证
Context-Dependent Implicit Authentication for Wearable Device User
论文作者
论文摘要
由于市场可穿戴设备在各种服务中变得越来越受欢迎，包括进行金融交易，访问汽车等。它们基于用户的各种私人信息提供，因此此信息的安全性变得非常重要。但是，在这种物联网世界（IoT）世界中，用户经常充斥着引脚和密码。此外，由于感应和计算功能有限，基于面部或手指识别的硬二维（例如面部或手指识别）无法适应市场可穿戴设备。因此，使用较不明智的软生物测量数据可以从市场可穿戴设备中获取，为可穿戴设备开发无负担的隐式身份验证机制是时候了。在这项工作中，我们利用心率，步态和呼吸音频信号提出了一个依赖上下文的基于上下文的可穿戴身份验证系统。 From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \pm 0.07$, $F_1$ score of $0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower confidence threshold of 0.52, which shows the promise of this work.
As market wearables are becoming popular with a range of services, including making financial transactions, accessing cars, etc. that they provide based on various private information of a user, security of this information is becoming very important. However, users are often flooded with PINs and passwords in this internet of things (IoT) world. Additionally, hard-biometric, such as facial or finger recognition, based authentications are not adaptable for market wearables due to their limited sensing and computation capabilities. Therefore, it is a time demand to develop a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from the market wearables. In this work, we present a context-dependent soft-biometric-based wearable authentication system utilizing the heart rate, gait, and breathing audio signals. From our detailed analysis, we find that a binary support vector machine (SVM) with radial basis function (RBF) kernel can achieve an average accuracy of $0.94 \pm 0.07$, $F_1$ score of $0.93 \pm 0.08$, an equal error rate (EER) of about $0.06$ at a lower confidence threshold of 0.52, which shows the promise of this work.
