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Computer vision has witnessed several advances in recent years, withunprecedented performance provided by deep representation learning research.Image formats thus appear attractive to other fields such as malware detection,where deep learning on images alleviates the need for comprehensivelyhand-crafted features generalising to different malware variants. We postulatethat this research direction could become the next frontier in Android malwaredetection, and therefore requires a clear roadmap to ensure that new approachesindeed bring novel contributions. We contribute with a first building block bydeveloping and assessing a baseline pipeline for image-based malware detectionwith straightforward steps. We propose DexRay, which converts the bytecode ofthe app DEX files into grey-scale "vector" images and feeds them to a1-dimensional Convolutional Neural Network model. We view DexRay asfoundational due to the exceedingly basic nature of the design choices,allowing to infer what could be a minimal performance that can be obtained withimage-based learning in malware detection. The performance of DexRay evaluatedon over 158k apps demonstrates that, while simple, our approach is effectivewith a high detection rate(F1-score= 0.96). Finally, we investigate the impactof time decay and image-resizing on the performance of DexRay and assess itsresilience to obfuscation. This work-in-progress paper contributes to thedomain of Deep Learning based Malware detection by providing a sound, simple,yet effective approach (with available artefacts) that can be the basis toscope the many profound questions that will need to be investigated to fullydevelop this domain.

DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode