Enabling Finger-Touch-Based Mobile User Authentication via Physical Vibrations on IoT Devices
Enabling Finger-Touch-Based Mobile User Authentication via Physical Vibrations on IoT Devices
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DOI：
10.1109/tmc.2021.3057083
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发表时间：
2021-02
影响因子：
7.9
通讯作者：
X. Yang;Song Yang;Jian Liu;Chen Wang;Yingying Chen;Nitesh Saxena
中科院分区：
文献类型：
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作者：
X. Yang;Song Yang;Jian Liu;Chen Wang;Yingying Chen;Nitesh Saxena
这项工作使移动的用户认证通过手指输入无处不在的表面上利用低成本的物理振动。我们提出的系统将手指输入认证扩展到触摸屏之外的任何物联网设备的固体表面（例如，智能接入系统和物联网设备）。与基于密码或生物识别的解决方案不同，它将密码、行为和生理特征以及表面依赖性集成在一起，以提供低成本、有形和增强的安全解决方案。所提出的系统建立在具有振动信号的触摸感测技术的基础上，该触摸感测技术可以在由各种材料构成的表面上操作。开发了新的算法来区分细粒度的手指输入，并通过在频域中提取独特的特征来捕获行为和生理特征，包括接触面积，触摸力，该系统使用单对低成本便携式振动马达和接收器来实现，该振动马达和接收器可以容易地附接到任何表面（例如，门板、炉灶或器具）。大量的实验表明，我们的系统可以以高准确度认证用户（例如，在两次试验中超过97%），低假阳性率（例如，小于2%），并对各种类型的攻击具有鲁棒性。
This work enables mobile user authentication via finger inputs on ubiquitous surfaces leveraging low-cost physical vibration. The system we proposed extends finger-input authentication beyond touch screens to any solid surface for IoT devices (e.g., smart access systems and IoT appliances). Unlike passcode or biometrics-based solutions, it integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. The proposed system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. New algorithms are developed to discriminate fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features in the frequency domain to capture both behavioral and physiological characteristics including contacting area, touching force, and etc. The system is implemented using a single pair of low-cost portable vibration motor and receiver that can be easily attached to any surface (e.g., a door panel, a stovetop or an appliance). Extensive experiments demonstrate that our system can authenticate users with high accuracy (e.g., more than 97 percent within two trials), low false positive rate (e.g., less 2 percent) and is robust to various types of attacks.