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dc.contributor.authorBasurto, Nuño
dc.contributor.authorGarcía-Prieto, Diego
dc.contributor.authorQuintián, Héctor
dc.contributor.authorUrda Muñoz, Daniel
dc.contributor.authorCalvo-Rolle, José Luis
dc.contributor.authorCorchado, Emilio
dc.date.accessioned2024-05-24T10:55:41Z
dc.date.issued2024-05-20
dc.identifier.citationNuño Basurto, Diego García-Prieto, Héctor Quintián, Daniel Urda, José Luis Calvo-Rolle, Emilio Corchado, Beta-Hebbian Learning to enhance unsupervised exploratory visualizations of Android malware families, Logic Journal of the IGPL, Volume 32, Issue 2, April 2024, Pages 306–320, https://doi.org/10.1093/jigpal/jzae014es_ES
dc.identifier.issn1368-9894
dc.identifier.urihttp://hdl.handle.net/2183/36613
dc.descriptionThis is a pre-copyedited, author-produced version of an article accepted for publication in Logic Journal of the IGPL following peer review. The version of record Nuño Basurto, Diego García-Prieto, Héctor Quintián, Daniel Urda, José Luis Calvo-Rolle, Emilio Corchado, Beta-Hebbian Learning to enhance unsupervised exploratory visualizations of Android malware families, Logic Journal of the IGPL, Volume 32, Issue 2, April 2024, Pages 306–320 is available online at: https://doi.org/10.1093/jigpal/jzae014es_ES
dc.description.abstract[Abstract] As it is well known, mobile phones have become a basic gadget for any individual that usually stores sensitive information. This mainly motivates the increase in the number of attacks aimed at jeopardizing smartphones, being an extreme concern above all on Android OS, which is the most popular platform in the market. Consequently, a strong effort has been devoted for mitigating mentioned incidents in recent years, even though few researchers have addressed the application of visualization techniques for the analysis of malware. Within this field, the present work proposes the extension of a new technique called Hybrid Unsupervised Exploratory Plots to visualize Android malware datasets. More precisely, the novel Beta-Hebbian Learning (BHL) method is applied for the first time and validated under the frame of Hybrid Unsupervised Exploratory Plots, in conjunction with clustering methods. The informative visualization achieved provides a picture of the structure of the malware families, allowing subsequent analysis of their organization. To validate the Hybrid Unsupervised Exploratory Plot extension and its tuning, the popular Android Malware Genome dataset has been used in the experimental setting. Promising results have been obtained, suggesting that BHL applied in combination with clustering techniques in Hybrid Unsupervised Exploratory Plots are a viable resource for the visualization of malware families.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherOxford University Presses_ES
dc.relation.urihttps://doi.org/10.1093/jigpal/jzae014es_ES
dc.subjectAndroid malwarees_ES
dc.subjectMalware familieses_ES
dc.subjectExploratory projection pursuites_ES
dc.subjectClusteringes_ES
dc.subject3D visualizationes_ES
dc.titleBeta-Hebbian Learning to enhance unsupervised exploratory visualizations of Android malware familieses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/embargoedAccesses_ES
dc.date.embargoEndDate2025-03-20es_ES
dc.date.embargoLift2025-03-20
UDC.journalTitleLogic Journal of the IGPLes_ES
UDC.volume32es_ES
UDC.issue2es_ES
UDC.startPage306es_ES
UDC.endPage320es_ES
dc.identifier.doihttps://doi.org/10.1093/jigpal/jzae014


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