Neural visualization of android malware families

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.