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Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices
Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mo...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271781/ https://www.ncbi.nlm.nih.gov/pubmed/34283149 http://dx.doi.org/10.3390/s21134592 |
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author | Zeng, Xin Zhang, Xiaomei Yang, Shuqun Shi, Zhicai Chi, Chihung |
author_facet | Zeng, Xin Zhang, Xiaomei Yang, Shuqun Shi, Zhicai Chi, Chihung |
author_sort | Zeng, Xin |
collection | PubMed |
description | Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size. |
format | Online Article Text |
id | pubmed-8271781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82717812021-07-11 Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices Zeng, Xin Zhang, Xiaomei Yang, Shuqun Shi, Zhicai Chi, Chihung Sensors (Basel) Article Implicit authentication mechanisms are expected to prevent security and privacy threats for mobile devices using behavior modeling. However, recently, researchers have demonstrated that the performance of behavioral biometrics is insufficiently accurate. Furthermore, the unique characteristics of mobile devices, such as limited storage and energy, make it subject to constrained capacity of data collection and processing. In this paper, we propose an implicit authentication architecture based on edge computing, coined Edge computing-based mobile Device Implicit Authentication (EDIA), which exploits edge-based gait biometric identification using a deep learning model to authenticate users. The gait data captured by a device’s accelerometer and gyroscope sensors is utilized as the input of our optimized model, which consists of a CNN and a LSTM in tandem. Especially, we deal with extracting the features of gait signal in a two-dimensional domain through converting the original signal into an image, and then input it into our network. In addition, to reduce computation overhead of mobile devices, the model for implicit authentication is generated on the cloud server, and the user authentication process also takes place on the edge devices. We evaluate the performance of EDIA under different scenarios where the results show that i) we achieve a true positive rate of 97.77% and also a 2% false positive rate; and ii) EDIA still reaches high accuracy with limited dataset size. MDPI 2021-07-05 /pmc/articles/PMC8271781/ /pubmed/34283149 http://dx.doi.org/10.3390/s21134592 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zeng, Xin Zhang, Xiaomei Yang, Shuqun Shi, Zhicai Chi, Chihung Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title | Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title_full | Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title_fullStr | Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title_full_unstemmed | Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title_short | Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices |
title_sort | gait-based implicit authentication using edge computing and deep learning for mobile devices |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271781/ https://www.ncbi.nlm.nih.gov/pubmed/34283149 http://dx.doi.org/10.3390/s21134592 |
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