El sandboxing ha sido usado de manera regular para analizar muestras de software y determinar si estas contienen propiedades o comportamientos sospechosos. A pesar de que el sandboxing es una técnica poderosa para desarrollar análisis de malware, esta requiere que un analista de malware desarrolle un análisis riguroso de los resultados para determinar la naturaleza de la muestra: goodware o malware. Este artículo propone dos modelos de aprendizaje automáticos capaces de clasificar muestras con base a un análisis de firmas o permisos extraídos por medio de Cuckoo sandbox, Androguard y VirusTotal
Sandboxing has been used regularly to analyze software samples and determine if these contain suspicious properties or behaviors. Even if sandboxing is a powerful technique to perform malware analysis, it requires that a malware analyst performs a rigorous analysis of the results to determine the nature of the sample: goodware or malware. This paper proposes two machine learning models able to classify samples based on signatures and permissions obtained through Cuckoo sandbox, Androguard and VirusTotal. The developed models are also tested obtaining an acceptable percentage of correctly classified samples, being in this way useful tools for a malware analyst. A proposal of architecture for an IoT sentinel that uses one of the developed machine learning model is also showed. Finally, different approaches, perspectives, and challenges about the use of sandboxing and machine learning by security teams in State security agencies are also shared.
