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

SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection