Malware Detection by Eating a Whole EXE
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Type of Work9 pages
conference papers and proceedings
Citation of Original PublicationEdward 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/16422/15577
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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.