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Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data
This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284384/ https://www.ncbi.nlm.nih.gov/pubmed/32413952 http://dx.doi.org/10.3390/s20102768 |
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author | Vlădăreanu, Victor Voiculescu, Valentin-Gabriel Grosu, Vlad-Alexandru Vlădăreanu, Luige Travediu, Ana-Maria Yan, Hao Wang, Hongbo Ruse, Laura |
author_facet | Vlădăreanu, Victor Voiculescu, Valentin-Gabriel Grosu, Vlad-Alexandru Vlădăreanu, Luige Travediu, Ana-Maria Yan, Hao Wang, Hongbo Ruse, Laura |
author_sort | Vlădăreanu, Victor |
collection | PubMed |
description | This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware. |
format | Online Article Text |
id | pubmed-7284384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-72843842020-08-13 Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data Vlădăreanu, Victor Voiculescu, Valentin-Gabriel Grosu, Vlad-Alexandru Vlădăreanu, Luige Travediu, Ana-Maria Yan, Hao Wang, Hongbo Ruse, Laura Sensors (Basel) Article This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware. MDPI 2020-05-13 /pmc/articles/PMC7284384/ /pubmed/32413952 http://dx.doi.org/10.3390/s20102768 Text en © 2020 by the authors. 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Vlădăreanu, Victor Voiculescu, Valentin-Gabriel Grosu, Vlad-Alexandru Vlădăreanu, Luige Travediu, Ana-Maria Yan, Hao Wang, Hongbo Ruse, Laura Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title | Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title_full | Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title_fullStr | Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title_full_unstemmed | Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title_short | Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data |
title_sort | detection of anomalous behavior in modern smartphones using software sensor-based data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284384/ https://www.ncbi.nlm.nih.gov/pubmed/32413952 http://dx.doi.org/10.3390/s20102768 |
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