Malware Detection in Android-based Mobile Environments using Optimum-Path Forest
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/ICMLA.2015.72 http://hdl.handle.net/11449/161766 |
Resumo: | Nowadays, people use smartphones and tablets with the very same purposes as desktop computers: web browsing, social networking and home-banking, just to name a few. However, we are often facing the problem of keeping our information protected and trustworthy. As a result of their popularity and functionality, mobile devices are a growing target for malicious activities. In such context, mobile malwares have gained significant ground since the emergence and growth of smartphones and handheld devices, becoming a real threat. In this paper, we introduced a recently developed pattern recognition technique called Optimum-Path Forest in the context of malware detection, as well we present DroidWare, a new public dataset to foster the research on mobile malware detection. In addition, we also proposed to use Restricted Boltzmann Machines for unsupervised feature learning in the context of malware identification. |
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Repositório Institucional da UNESP |
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Malware Detection in Android-based Mobile Environments using Optimum-Path ForestOptimum-Path ForestRestricted Boltzmann MachinesMalware DetectionNowadays, people use smartphones and tablets with the very same purposes as desktop computers: web browsing, social networking and home-banking, just to name a few. However, we are often facing the problem of keeping our information protected and trustworthy. As a result of their popularity and functionality, mobile devices are a growing target for malicious activities. In such context, mobile malwares have gained significant ground since the emergence and growth of smartphones and handheld devices, becoming a real threat. In this paper, we introduced a recently developed pattern recognition technique called Optimum-Path Forest in the context of malware detection, as well we present DroidWare, a new public dataset to foster the research on mobile malware detection. In addition, we also proposed to use Restricted Boltzmann Machines for unsupervised feature learning in the context of malware identification.Sao Paulo State Univ, Dept Comp, BR-17033360 Bauru, SP, BrazilSao Paulo State Univ, Dept Comp, BR-17033360 Bauru, SP, BrazilElsevier B.V.Universidade Estadual Paulista (Unesp)Costa, Kelton A. P. da [UNESP]Silva, Luis A. da [UNESP]Martins, Guilherme B. [UNESP]Rosa, Gustavo H. [UNESP]Pereira, Clayton R. [UNESP]Papa, Joao P. [UNESP]IEEE2018-11-26T16:48:32Z2018-11-26T16:48:32Z2015-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject754-759http://dx.doi.org/10.1109/ICMLA.2015.722015 Ieee 14th International Conference On Machine Learning And Applications (icmla). Amsterdam: Elsevier Science Bv, p. 754-759, 2015.http://hdl.handle.net/11449/16176610.1109/ICMLA.2015.72WOS:000380483600136Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2015 Ieee 14th International Conference On Machine Learning And Applications (icmla)info:eu-repo/semantics/openAccess2021-10-23T21:47:00Zoai:repositorio.unesp.br:11449/161766Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T21:47Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
title |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
spellingShingle |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest Costa, Kelton A. P. da [UNESP] Optimum-Path Forest Restricted Boltzmann Machines Malware Detection |
title_short |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
title_full |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
title_fullStr |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
title_full_unstemmed |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
title_sort |
Malware Detection in Android-based Mobile Environments using Optimum-Path Forest |
author |
Costa, Kelton A. P. da [UNESP] |
author_facet |
Costa, Kelton A. P. da [UNESP] Silva, Luis A. da [UNESP] Martins, Guilherme B. [UNESP] Rosa, Gustavo H. [UNESP] Pereira, Clayton R. [UNESP] Papa, Joao P. [UNESP] IEEE |
author_role |
author |
author2 |
Silva, Luis A. da [UNESP] Martins, Guilherme B. [UNESP] Rosa, Gustavo H. [UNESP] Pereira, Clayton R. [UNESP] Papa, Joao P. [UNESP] IEEE |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Costa, Kelton A. P. da [UNESP] Silva, Luis A. da [UNESP] Martins, Guilherme B. [UNESP] Rosa, Gustavo H. [UNESP] Pereira, Clayton R. [UNESP] Papa, Joao P. [UNESP] IEEE |
dc.subject.por.fl_str_mv |
Optimum-Path Forest Restricted Boltzmann Machines Malware Detection |
topic |
Optimum-Path Forest Restricted Boltzmann Machines Malware Detection |
description |
Nowadays, people use smartphones and tablets with the very same purposes as desktop computers: web browsing, social networking and home-banking, just to name a few. However, we are often facing the problem of keeping our information protected and trustworthy. As a result of their popularity and functionality, mobile devices are a growing target for malicious activities. In such context, mobile malwares have gained significant ground since the emergence and growth of smartphones and handheld devices, becoming a real threat. In this paper, we introduced a recently developed pattern recognition technique called Optimum-Path Forest in the context of malware detection, as well we present DroidWare, a new public dataset to foster the research on mobile malware detection. In addition, we also proposed to use Restricted Boltzmann Machines for unsupervised feature learning in the context of malware identification. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-01-01 2018-11-26T16:48:32Z 2018-11-26T16:48:32Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/ICMLA.2015.72 2015 Ieee 14th International Conference On Machine Learning And Applications (icmla). Amsterdam: Elsevier Science Bv, p. 754-759, 2015. http://hdl.handle.net/11449/161766 10.1109/ICMLA.2015.72 WOS:000380483600136 |
url |
http://dx.doi.org/10.1109/ICMLA.2015.72 http://hdl.handle.net/11449/161766 |
identifier_str_mv |
2015 Ieee 14th International Conference On Machine Learning And Applications (icmla). Amsterdam: Elsevier Science Bv, p. 754-759, 2015. 10.1109/ICMLA.2015.72 WOS:000380483600136 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2015 Ieee 14th International Conference On Machine Learning And Applications (icmla) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
754-759 |
dc.publisher.none.fl_str_mv |
Elsevier B.V. |
publisher.none.fl_str_mv |
Elsevier B.V. |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1792961562969899008 |