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This paper describes EMBER: a labeled benchmark dataset for training machinelearning models to statically detect malicious Windows portable executablefiles. The dataset includes features extracted from 1.1M binary files: 900Ktraining samples (300K malicious, 300K benign, 300K unlabeled) and 200K testsamples (100K malicious, 100K benign). To accompany the dataset, we alsorelease open source code for extracting features from additional binaries sothat additional sample features can be appended to the dataset. This datasetfills a void in the information security machine learning community: abenign/malicious dataset that is large, open and general enough to coverseveral interesting use cases. We enumerate several use cases that weconsidered when structuring the dataset. Additionally, we demonstrate one usecase wherein we compare a baseline gradient boosted decision tree model trainedusing LightGBM with default settings to MalConv, a recently publishedend-to-end (featureless) deep learning model for malware detection. Resultsshow that even without hyper-parameter optimization, the baseline EMBER modeloutperforms MalConv. The authors hope that the dataset, code and baseline modelprovided by EMBER will help invigorate machine learning research for malwaredetection, in much the same way that benchmark datasets have advanced computervision research.

EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models