Malware Detection by Eating a Whole EXE
It addresses malware detection for cybersecurity, but is incremental as it builds on existing methods for sequence processing.
The paper tackled malware detection from raw byte sequences, presenting a neural network with linear complexity in sequence length and interpretable sub-region identification.
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.