RT info:eu-repo/semantics/article
T1 Delving into Android Malware Families with a Novel Neural Projection Method
A1 Vega Vega, Rafael Alejandro
A1 Quintián, Héctor
A1 Cambra Baseca, Carlos
A1 Basurto Hornillos, Nuño
A1 Herrero Cosío, Álvaro
A1 Calvo-Rolle, José Luis
K1 Informática
K1 Computer science
AB 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.
PB Hindawi
SN 1076-2787
YR 2019
FD 2019-06
LK http://hdl.handle.net/10259/7260
UL http://hdl.handle.net/10259/7260
LA eng
NO This work is partially supported by Instituto Nacional de Ciberseguridad (INCIBE) and developed by Research Institute of Applied Sciences in Cybersecurity (RIASC).
DS Repositorio Institucional de la Universidad de Burgos
RD 23-nov-2024