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Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails,...

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Detalles Bibliográficos
Autores principales: Mekruksavanich, Sakorn, Jitpattanakul, Anuchit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625098/
https://www.ncbi.nlm.nih.gov/pubmed/34833591
http://dx.doi.org/10.3390/s21227519
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author Mekruksavanich, Sakorn
Jitpattanakul, Anuchit
author_facet Mekruksavanich, Sakorn
Jitpattanakul, Anuchit
author_sort Mekruksavanich, Sakorn
collection PubMed
description Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.
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spelling pubmed-86250982021-11-27 Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing Mekruksavanich, Sakorn Jitpattanakul, Anuchit Sensors (Basel) Article Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework. MDPI 2021-11-12 /pmc/articles/PMC8625098/ /pubmed/34833591 http://dx.doi.org/10.3390/s21227519 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
Mekruksavanich, Sakorn
Jitpattanakul, Anuchit
Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title_full Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title_fullStr Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title_full_unstemmed Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title_short Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
title_sort deep learning approaches for continuous authentication based on activity patterns using mobile sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625098/
https://www.ncbi.nlm.nih.gov/pubmed/34833591
http://dx.doi.org/10.3390/s21227519
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