论文标题
智能手腕可穿戴物联网设备的手腕运动和安全性问题的数字识别
Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices
论文作者
论文摘要
在本文中，我们研究了与手腕可穿戴设备相关的潜在安全漏洞。常见可穿戴设备上的硬件组件包括加速度计和陀螺仪以及其他传感器。我们证明，加速度计和陀螺仪可以拾取足够独特的手腕运动信息来识别用户编写的数字。使用数字零或数字的数据集的数据集，我们构建了一个机器学习模型，以根据手腕的运动正确识别编写的数字。我们的模型在看不见的测试集上的性能导致接收器操作特征（AUROC）曲线的区域为1.00。在实时预测十个写作样本时，将我们的模型加载到制造的设备上会产生100％的精度。该模型通过手腕运动和方向变化正确识别所有数字的能力引起了安全问题。我们的结果表明，邪恶的人可能能够从手腕可穿戴设备中获取基于敏感的数字信息，例如社会保障，信用卡和病历号码。
In this paper, we investigate a potential security vulnerability associated with wrist wearable devices. Hardware components on common wearable devices include an accelerometer and gyroscope, among other sensors. We demonstrate that an accelerometer and gyroscope can pick up enough unique wrist movement information to identify digits being written by a user. With a data set of 400 writing samples, of either the digit zero or the digit one, we constructed a machine learning model to correctly identify the digit being written based on the movements of the wrist. Our model's performance on an unseen test set resulted in an area under the receiver operating characteristic (AUROC) curve of 1.00. Loading our model onto our fabricated device resulted in 100% accuracy when predicting ten writing samples in real-time. The model's ability to correctly identify all digits via wrist movement and orientation changes raises security concerns. Our results imply that nefarious individuals may be able to gain sensitive digit based information such as social security, credit card, and medical record numbers from wrist wearable devices.
