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Andrés Navarro Cadavid ;Sistemas & Telemática 2016, 14 37

Autor: Christian Camilo Urcuqui López

Fuente: http://www.redalyc.org/articulo.oa?id=411546577003


Introducción



Sistemas & Telemática ISSN: 1692-5238 EditorSyT@icesi.edu.co Universidad ICESI Colombia Urcuqui López, Christian Camilo; Navarro Cadavid, Andrés Framework for malware analysis in Android Sistemas & Telemática, vol.
14, núm.
37, 2016, pp.
45-56 Universidad ICESI Cali, Colombia Available in: http:--www.redalyc.org-articulo.oa?id=411546577003 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Urcuqui, C.
& Navarro, A.
(2016).
Framework for malware analysis in Android.
Sistemas & Telemática, 14(37), 45-56 Discussion Paper - Articulo de Reflexión - Tipo 2 Framework for malware analysis in Android Christian Camilo Urcuqui López - ccurcuqui@icesi.edu.co Andrés Navarro Cadavid - anavarro@icesi.edu.co Grupo de Investigación i2t, Universidad Icesi, Cali, Colombia ABSTRACT Android is a open source operating system with more than a billion of users, including all kind of devices (cell phones, TV, smart watch, etc).
The amount of sensitive data “using” this technologies has increased the cyber criminals interest to develop tools and techniques to acquire that information or to disrupt the device’s smooth operation. Despite several solutions are able to guarantee an adequate level of security, day by day the hackers skills grows up (because of their growing experience), what means a permanent challenge for security tools developers.
As a response, several members of the research community are using artificial intelligence tools for Android security, particularly machine learning techniques to classify between healthy and malicious apps; from an analytic review of those works, this paper propose a static analysis framework and machine learning to do that classification. KEYWORDS Framework; machine learning; security; Google; m...





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