2026-06-29T21:08:06Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/72522023-01-18T01:05:24Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848

Vega Vega, Rafael Alejandro Quintián, Héctor Calvo-Rolle, José Luis Herrero Cosío, Álvaro Corchado, Emilio 2023-01-17T11:47:46Z 2023-01-17T11:47:46Z 2019-04 1367-0751 http://hdl.handle.net/10259/7252 10.1093/jigpal/jzy030 1368-9894 This research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android malware families. Interesting conclusions are obtained from the real-life data set under analysis. application/pdf eng Oxford University Press Logic Journal of the IGPL. 2019, V. 27, n. 2, p. 160-176 https://doi.org/10.1093/jigpal/jzy030 Android malware Malware families Dimensionality reduction Artificial neural networks Informática Computer science Gaining deep knowledge of Android malware families through dimensionality reduction techniques info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/openAccess Logic Journal of the IGPL 27 2 160 176