  In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million)
dataset: a large-scale dataset consisting of nearly 20 million files with
pre-extracted features and metadata, high-quality labels derived from multiple
sources, information about vendor detections of the malware samples at the time
of collection, and additional ``tags'' related to each malware sample to serve
as additional targets. In addition to features and metadata, we also provide
approximately 10 million ``disarmed'' malware samples -- samples with both the
optional\_headers.subsystem and file\_header.machine flags set to zero -- that
may be used for further exploration of features and detection strategies. We
also provide Python code to interact with the data and features, as well as
baseline neural network and gradient boosted decision tree models and their
results, with full training and evaluation code, to serve as a starting point
for further experimentation.
