Malware Detection by Eating a Whole EXE
Type of Work
9 pagesText
conference papers and proceedings
Citation of Original Publication
Edward Raff, Jon Barker, Jared Sylvester, Robert Brandon, Bryan Catanzaro, Charles Nicholas, Malware Detection by Eating a Whole EXE, The Workshops of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018,https://aaai.org/ocs/index.php/WS/AAAIW18/paper/viewFile/16422/15577Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.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.