2026-10-29T17:17:50Zhttps://riubu.ubu.es/oai/request

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

00925njm 22002777a 4500 dc Vega Vega, Rafael Alejandro author Quintián, Héctor author Cambra Baseca, Carlos author Basurto Hornillos, Nuño author Herrero Cosío, Álvaro author Calvo-Rolle, José Luis author 2019-06 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. 1076-2787 http://hdl.handle.net/10259/7260 10.1155/2019/6101697 1099-0526 Delving into Android Malware Families with a Novel Neural Projection Method