2026-05-07T16:32:05Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/72652023-03-17T12:41:39Zcom_10259_3847com_10259_5086com_10259_2604col_10259_3848
Sedano, Javier
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600
González González, Silvia .
e56ee49e-5690-4b24-9a69-cc32d138718c
600
Chira, Camelia
254ce200-8fed-4d86-91cf-5cfa12beb061
600
Herrero Cosío, Álvaro
283
600
0000-0002-2444-5384
Corchado, Emilio
125
600
0000-0001-8560-3991
Villar, José Ramón
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600
2023-01-18T12:03:20Z
2023-01-18T12:03:20Z
2017-02
1367-0751
http://hdl.handle.net/10259/7265
10.1093/jigpal/jzw046
1368-9894
In recent years, mobile devices such as smartphones, tablets and wearables have become the new paradigm of user–computer
interaction. The increasing use and adoption of such devices is also leading to an increased number of potential security risks.
The spread of mobile malware, particularly on popular and open platforms such as Android, has become a major concern.
This paper focuses on the bad-intentioned Android apps by addressing the problem of selecting the key features of such
software that support the characterization of such malware. The accurate detection and characterization of this software is
still an open challenge, mainly due to its ever-changing nature and the open distribution channels of Android apps. Maximum
relevance minimum redundancy and evolutionary algorithms guided by information correlation measures have been applied
for feature selection on the well-known Android Malware Genome (Malgenome) dataset, attaining interesting results on the
most informative features for the characterization of representative families of existing Android malware.
This research has been partially supported through the project of the Spanish Ministry of Economy and Competitiveness RTC-2014-3059-4. The authors would also like to thank the BIO/BU01/15 and the Spanish Ministry of Science and Innovation PID 560300-2009-11.
application/pdf
eng
Oxford University Press
Logic Journal of the IGPL. 2017, V. 25, n. 1, p. 54-66
https://doi.org/10.1093/jigpal/jzw046
info:eu-repo/grantAgreement/MINECO//RTC-2014-3059-4G09226606CASTILLA Y LEON/ES/SISTEMA MULTI-AGENTE INTELIGENTE PARA LA RECARGA DE VEHÍCULO ELÉCTRICO CUMPLIENDO ISO/IEC 15118 1 y 2 (PROYECTO MAS-I-REVE)/
info:eu-repo/grantAgreement/MINECO//RTC-2014-3059-4A09019936CASTILLA Y LEON/ES/SISTEMA MULTI-AGENTE INTELIGENTE PARA LA RECARGA DE VEHÍCULO ELÉCTRICO CUMPLIENDO ISO/IEC 15118 1 y 2 (PROYECTO MAS-I-REVE)/
info:eu-repo/grantAgreement/Junta de Castilla y León//BIO%2FBU01%2F15//Utilización de acelerómetros triaxiales para la caracterización de los patrones de movimiento en pacientes epilépticos/
info:eu-repo/grantAgreement/MICINN/Plan Estatal de Investigación Científica y Técnica y de Innovación 2008-2011/PID-560300-2009-11/ES/La excelencia española en rapid manufacturing, desarrollo y aplicación de la tecnología de deformación incremental de segunda generación para aplicaciones estratégicas/ISF_2G
Feature selection
Evolutionary computation
Max-relevance min-redundancy criteria
Information correlation coefficient
Android
Malware
Informática
Computer science
Key features for the characterization of Android malware families
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
info:eu-repo/semantics/openAccess
Logic Journal of IGPL
25
1
54
66
THUMBNAIL
Herrero-igpl_2017.pdf.jpg
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https://riubu.ubu.es/bitstream/10259/7265/3/Herrero-igpl_2017.pdf.jpg
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LICENSE
license.txt
license.txt
text/plain; charset=utf-8
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https://riubu.ubu.es/bitstream/10259/7265/2/license.txt
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ORIGINAL
Herrero-igpl_2017.pdf
Herrero-igpl_2017.pdf
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https://riubu.ubu.es/bitstream/10259/7265/1/Herrero-igpl_2017.pdf
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10259/7265
oai:riubu.ubu.es:10259/7265
2023-03-17 13:41:39.794
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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