Abstract
Undoubtedly, mobile devices (mainly smartphones and tablets up to now) have become the new paradigm of user-computer interaction. The use of such gadgets is increasing to unexpected figures and, at the same time, the number of potential security risks. This paper focuses on the bad-intentioned Android apps, as it is still the most widely used operating systems for such devices. Accurate detection of this malware remains an open challenge, mainly due to the ever-changing nature of malware and the “open” distribution channel of Android apps through Google Play. Present work uses feature selection for the identification of those features that may help in characterizing mobile Android-based malware. Maximum Relevance Minimum Redundancy and genetic algorithms guided by information correlation measures have been applied to the Android Malware Genome (Malgenome) dataset, attaining interesting results on the most informative features for the characterization of representative families of existing Android malware.
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References
Statista - The Statistics Portal, http://www.statista.com/statistics/266219/global-smartphone-sales-since-1st-quarter-2009-by-operating-system/
AppBrain Stats, http://www.appbrain.com/stats/stats-index
F-Secure: Q1 2014 Mobile Threat Report (2015)
Yajin, Z., Xuxian, J.: Dissecting android malware: characterization and evolution. In: 2012 IEEE Symposium on Security and Privacy 5, 95–109 (2012)
Malgenome Project, http://www.malgenomeproject.org/
Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, A.: Machine learning in bioinformatics. Brief. Bioinform 7(1), 86–112 (2006)
Ding, C., Peng, H.: Minimum redundancy feature selection from microarray gene expression data. J. Bioinform. Comput. Biol. 3(02), 185–205 (2005)
Liu, H., Liu, L., Zhang, H.: Ensemble gene selection by grouping for microarray data classification. J. Biomed. Inform. 43(1), 81–87 (2010)
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Hatami, N., Chira, C.: Diverse accurate feature selection for microarray cancer diagnosis. Intell. Data Anal. 17(4), 697–716 (2013)
Vinod, P., Laxmi, V., Gaur, M.S., Naval, S., Faruki, P.: MCF: MultiComponent Features for malware analysis. In: 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), 2013, pp. 1076–1081 (2013)
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P.G.: On the automatic categorisation of android applications. In: 2012 IEEE Consumer Communications and Networking Conference (CCNC), pp. 149–153 (2012)
Sanz, B., Santos, I., Laorden, C., Ugarte-Pedrero, X., Bringas, P., Álvarez, G.: PUMA: Permission Usage to Detect Malware in Android. In: Herrero Á., Snášel V., Abraham A., Zelinka I., Baruque B., Quintián H., Calvo J.L., Sedano J., Corchado E. (eds.) International Joint Conference CISIS’12-ICEUTE´12-SOCO´12 Special Sessions, vol. 189. Springer, Berlin, Heidelberg. pp. 289–298 (2013)
Cover, T.M., Thomas, J.A.: Elements of Information Theory. Wiley, New York (1991)
Acknowledgments
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/BU09/14 and the Spanish Ministry of Science and Innovation PID 560300-2009-11.
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Sedano, J., Chira, C., González, S., Herrero, Á., Corchado, E., Villar, J.R. (2015). On the Selection of Key Features for Android Malware Characterization. In: Herrero, Á., Baruque, B., Sedano, J., Quintián, H., Corchado, E. (eds) International Joint Conference. CISIS 2015. Advances in Intelligent Systems and Computing, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-319-19713-5_15
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DOI: https://doi.org/10.1007/978-3-319-19713-5_15
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