An End-to-End Deep Learning Architecture for Classification of Malware's Binary Content

In traditional machine learning techniques for malware detection and classification, significant efforts are expended on manually designing features based on expertise and domain-specific knowledge. These solutions perform feature engineering in order to extract features that provide an abstract view of the software program. Thus, the usefulness of the classifier is roughly dependent on the ability of the domain experts to extract a set of descriptive features. Instead, we introduce a file agnostic end-to-end deep learning approach for malware classification from raw byte sequences without extracting hand-crafted features. It consists of two key components: (1) a denoising autoencoder that learns a hidden representation of the malware’s binary content; and (2) a dilated residual network as classifier. The experiments show an impressive performance, achieving almost 99% of accuracy classifying malware into families.