Deep Convolutional Malware Classifiers Can Learn from Raw Executables and Labels Only

Abstrakt

We propose and evaluate a simple convolutional deep neural network architecture detecting malicious Portable Executables (Windows executable files) by learning from their raw sequences of bytes and labels only, that is, without any domainspecific feature extraction nor preprocessing. On a dataset of 20 million unpacked half megabyte Portable Executables, such end-to-end approach achieves performance almost on par with the traditional machine learning pipeline based on handcrafted features of Avast.