Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Malware Detection by Eating a Whole EXE   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.

3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int