Title: Malware Detection by Eating a Whole EXE,
Abstract: In this work we introduce malware detection from raw byte sequences as a
fruitful research area to the larger machine learning community. Building a
neural network for such a problem presents a number of interesting challenges
that have not occurred in tasks such as image processing or NLP. In particular,
we note that detection from raw bytes presents a sequence problem with over two
million time steps and a problem where batch normalization appear to hinder the
learning process. We present our initial work in building a solution to tackle
this problem, which has linear complexity dependence on the sequence length,
and allows for interpretable sub-regions of the binary to be identified. In
doing so we will discuss the many challenges in building a neural network to
process data at this scale, and the methods we used to work around them.