2024-03-28T22:33:07Zhttps://riubu.ubu.es/oai/requestoai: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