2026-04-19T19:59:39Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72602023-01-18T01:05:32Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Vega Vega, Rafael Alejandro Quintián, Héctor Cambra Baseca, Carlos Basurto Hornillos, Nuño Herrero Cosío, Álvaro Calvo-Rolle, José Luis 2023-01-17T12:50:07Z 2023-01-17T12:50:07Z 2019-06 1076-2787 http://hdl.handle.net/10259/7260 10.1155/2019/6101697 1099-0526 Present 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. This work is partially supported by Instituto Nacional de Ciberseguridad (INCIBE) and developed by Research Institute of Applied Sciences in Cybersecurity (RIASC). application/pdf eng Hindawi Complexity. 2019, V. 2019, p. 1-10 https://doi.org/10.1155/2019/6101697 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Informática Computer science Delving into Android Malware Families with a Novel Neural Projection Method info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Complexity 2019 1 10