  Many efforts have been made to use various forms of domain knowledge in
malware detection. Currently there exist two common approaches to malware
detection without domain knowledge, namely byte n-grams and strings. In this
work we explore the feasibility of applying neural networks to malware
detection and feature learning. We do this by restricting ourselves to a
minimal amount of domain knowledge in order to extract a portion of the
Portable Executable (PE) header. By doing this we show that neural networks can
learn from raw bytes without explicit feature construction, and perform even
better than a domain knowledge approach that parses the PE header into explicit
features.
