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dc.contributor.authorVega Vega, Rafael Alejandro
dc.contributor.authorQuintián, Héctor
dc.contributor.authorCambra Baseca, Carlos 
dc.contributor.authorBasurto Hornillos, Nuño 
dc.contributor.authorHerrero Cosío, Álvaro 
dc.contributor.authorCalvo-Rolle, José Luis
dc.date.accessioned2023-01-17T12:50:07Z
dc.date.available2023-01-17T12:50:07Z
dc.date.issued2019-06
dc.identifier.issn1076-2787
dc.identifier.urihttp://hdl.handle.net/10259/7260
dc.description.abstractPresent research proposes the application of unsupervised and supervised machine-learning techniques to characterize Android malware families. More precisely, a novel unsupervised neural-projection method for dimensionality-reduction, namely, Beta Hebbian Learning (BHL), is applied to visually analyze such malware. Additionally, well-known supervised Decision Trees (DTs) are also applied for the first time in order to improve characterization of such families and compare the original features that are identified as the most important ones. The proposed techniques are validated when facing real-life Android malware data by means of the well-known and publicly available Malgenome dataset. Obtained results support the proposed approach, confirming the validity of BHL and DTs to gain deep knowledge on Android malware.en
dc.description.sponsorshipThis work is partially supported by Instituto Nacional de Ciberseguridad (INCIBE) and developed by Research Institute of Applied Sciences in Cybersecurity (RIASC).en
dc.format.mimetypeapplication/pdf
dc.language.isoenges
dc.publisherHindawies
dc.relation.ispartofComplexity. 2019, V. 2019, p. 1-10es
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subject.otherInformáticaes
dc.subject.otherComputer scienceen
dc.titleDelving into Android Malware Families with a Novel Neural Projection Methoden
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.relation.publisherversionhttps://doi.org/10.1155/2019/6101697es
dc.identifier.doi10.1155/2019/6101697
dc.identifier.essn1099-0526
dc.journal.titleComplexityen
dc.volume.number2019es
dc.page.initial1es
dc.page.final10es
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones


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