“Malware Detection by Eating a Whole EXE”, 2017-10-25 (; backlinks; similar):
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 appears 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.
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