Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/64532

Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices

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Title:Digit Recognition From Wrist Movements and Security Concerns with Smart Wrist Wearable IOT Devices
Authors:Leong, Lambert
Wiere, Sean
Keywords:Machine Learning and Cyber Threat Intelligence and Analytics
machine learning
security
wrist wearable
Date Issued:07 Jan 2020
Abstract: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.
Pages/Duration:8 pages
URI:http://hdl.handle.net/10125/64532
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.790
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Cyber Threat Intelligence and Analytics


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