Malware Detection by Eating a Whole EXE

Understand

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.