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Abstract

Due to the ever increasing amount and severity of attacks aimed at compromising smartphones in general, and Android devices in particular, much effort have been devoted in recent years to deal with such incidents. However, scant attention has been devoted to study the interplay between visualization techniques and Android malware detection. As an initial proposal, neural projection architectures are applied in present work to analyze malware apps data and characterize malware families. By the advanced and intuitive visualization, the proposed solution provides with an overview of the structure of the families dataset and ease the analysis of their internal organization. Dimensionality reduction based on unsupervised neural networks is performed on family information from the Android Malware Genome (Malgenome) dataset.

Keywords

Android malware Malware families Artificial neural networks Exploratory projection pursuit 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Alejandro González
    • 1
  • Álvaro Herrero
    • 1
  • Emilio Corchado
    • 2
  1. 1.Department of Civil EngineeringUniversity of BurgosBurgosSpain
  2. 2.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaSpain

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