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Machine learning models and dimensionality reduction for improving the Android malware detection

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dc.contributor.authorMoran, Pablo
dc.contributor.authorRobles Gómez, Antonio
dc.contributor.authorDuque Fernández, Andrés
dc.contributor.authorTobarra Abad, María de los Llanos
dc.contributor.authorPastor Vargas, Rafael
dc.date.accessioned2024-12-31T11:42:22Z
dc.date.available2024-12-31T11:42:22Z
dc.date.issued2024-12-23
dc.descriptionLa versión registrada de este artículo, publicado por primera vez en PeerJ Computer Science 10:e2616, está disponible en línea en el sitio web del editor: https://doi.org/10.7717/peerj-cs.2616. The copyrighted version of this article, first published in PeerJ Computer Science 10:e2616, is available online at the publisher's website: https://doi.org/10.7717/peerj-cs.2616.
dc.description.abstractToday, a great number of attack opportunities for cybercriminals arise in Android, since it is one of the most used operating systems for many mobile applications. Hence, it is very important to anticipate these situations. To minimize this problem, the analysis of malware search applications is based on machine learning algorithms. Our work uses as a starting point the features proposed by the DREBIN project, which today constitutes a key reference in the literature, being the largest public Android malware dataset with labeled families. The authors only employ the support vector machine to determine whether a sample is malware or not. This work first proposes a new efficient dimensionality reduction of features, as well as the application of several supervised machine learning algorithms for prediction purposes. Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. This is just over 9% of the total number of features of DREBIN. It achieves an accuracy of 99.52%, a total precision of 96.91%, as well as a macro average F1-score of 96.99%.en
dc.description.versionversión publicada
dc.identifier.citationMorán P, Robles-Gómez A, Duque A, Tobarra L, Pastor-Vargas R. 2024. Machine learning models and dimensionality reduction for improving the Android malware detection. PeerJ Computer Science 10:e2616; https://doi.org/10.7717/peerj-cs.2616
dc.identifier.doihttps://doi.org/10.7717/peerj-cs.2616
dc.identifier.issn2376-5992
dc.identifier.urihttps://hdl.handle.net/20.500.14468/25079
dc.journal.titlePeerJ Computer Science
dc.journal.volume10
dc.language.isoen
dc.page.final22
dc.page.initial1
dc.publisherPeerJ
dc.relation.centerFacultades y escuelas::E.T.S. de Ingeniería Informática
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsMachine Learning algorithmsen
dc.subject.keywordsRandom Foresten
dc.subject.keywordssupervised feature selection techniquesen
dc.subject.keywordsfeature filtering techniquesen
dc.subject.keywordspredictive goodness metricsen
dc.titleMachine learning models and dimensionality reduction for improving the Android malware detectiones
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
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relation.isAuthorOfPublicationd6578720-2401-40cf-860c-92822eaf361a
relation.isAuthorOfPublicationb584f8a3-eb01-4a43-9ed7-5075b74224ae
relation.isAuthorOfPublicationf93103de-336d-47ac-886b-e2cbd425ed87
relation.isAuthorOfPublication.latestForDiscovery17556659-f434-4220-841d-aac35f492e62
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