On the Influence of Image Settings in Deep Learning-based Malware Detection

Considering the inadequacy of signature-based approaches for detecting malware, especially in the mobile environment, the research community is developing methodologies for detecting malware, especially using deep learning techniques, modeling applications like images. In state-of-the-art, several methods are proposed, each of one using a different kind of images and a different dimension of images: currently these are not standard settings for image preprocessing in Android malware detection. The aim of this paper is to compare different deep learning models performances to understand the best settings in terms of kind of image and image dimension. The idea is to trace a path in order to indicate the optimal settings for processing a dataset for malware detection using deep learning.