Gaining deep knowledge of Android malware families through dimensionality reduction techniques

This research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through t...

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Bibliographic Details
Authors: Vega Vega, Rafael Alejandro, Quintián, Héctor, Calvo-Rolle, José Luis, Herrero Cosío, Álvaro, Corchado, Emilio
Format: article
Status:Published version
Publication Date:2019
Country:España
Institution:Universidad de Burgos (UBU)
Repository:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/7252
Online Access:http://hdl.handle.net/10259/7252
Access Level:Open access
Keyword:Android malware
Malware families
Dimensionality reduction
Artificial neural networks
Informática
Computer science
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spelling Gaining deep knowledge of Android malware families through dimensionality reduction techniquesVega Vega, Rafael AlejandroQuintián, HéctorCalvo-Rolle, José LuisHerrero Cosío, ÁlvaroCorchado, EmilioAndroid malwareMalware familiesDimensionality reductionArtificial neural networksInformáticaComputer scienceThis research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android malware families. Interesting conclusions are obtained from the real-life data set under analysis.Oxford University Press202320232019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/7252reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésLogic Journal of the IGPL. 2019, V. 27, n. 2, p. 160-176https://doi.org/10.1093/jigpal/jzy030info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/72522026-05-28T07:56:11Z
dc.title.none.fl_str_mv Gaining deep knowledge of Android malware families through dimensionality reduction techniques
title Gaining deep knowledge of Android malware families through dimensionality reduction techniques
spellingShingle Gaining deep knowledge of Android malware families through dimensionality reduction techniques
Vega Vega, Rafael Alejandro
Android malware
Malware families
Dimensionality reduction
Artificial neural networks
Informática
Computer science
title_short Gaining deep knowledge of Android malware families through dimensionality reduction techniques
title_full Gaining deep knowledge of Android malware families through dimensionality reduction techniques
title_fullStr Gaining deep knowledge of Android malware families through dimensionality reduction techniques
title_full_unstemmed Gaining deep knowledge of Android malware families through dimensionality reduction techniques
title_sort Gaining deep knowledge of Android malware families through dimensionality reduction techniques
dc.creator.none.fl_str_mv Vega Vega, Rafael Alejandro
Quintián, Héctor
Calvo-Rolle, José Luis
Herrero Cosío, Álvaro
Corchado, Emilio
author Vega Vega, Rafael Alejandro
author_facet Vega Vega, Rafael Alejandro
Quintián, Héctor
Calvo-Rolle, José Luis
Herrero Cosío, Álvaro
Corchado, Emilio
author_role author
author2 Quintián, Héctor
Calvo-Rolle, José Luis
Herrero Cosío, Álvaro
Corchado, Emilio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Android malware
Malware families
Dimensionality reduction
Artificial neural networks
Informática
Computer science
topic Android malware
Malware families
Dimensionality reduction
Artificial neural networks
Informática
Computer science
description This research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android malware families. Interesting conclusions are obtained from the real-life data set under analysis.
publishDate 2019
dc.date.none.fl_str_mv 2019
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/7252
url http://hdl.handle.net/10259/7252
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Logic Journal of the IGPL. 2019, V. 27, n. 2, p. 160-176
https://doi.org/10.1093/jigpal/jzy030
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869412748830441472
score 15,300724

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