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In recent years there has been a shift from heuristics-based malwaredetection towards machine learning, which proves to be more robust in thecurrent heavily adversarial threat landscape. While we acknowledge machinelearning to be better equipped to mine for patterns in the increasingly highamounts of similar-looking files, we also note a remarkable scarcity of thedata available for similarity-targeted research. Moreover, we observe that thefocus in the few related works falls on quantifying similarity in malware,often overlooking the clean data. This one-sided quantification is especiallydangerous in the context of detection bypass. We propose to address thedeficiencies in the space of similarity research on binary files, starting fromEMBER - one of the largest malware classification data sets. We enhance EMBERwith similarity information as well as malware class tags, to enable furtherresearch in the similarity space. Our contribution is threefold: (1) we publishEMBERSim, an augmented version of EMBER, that includes similarity-informedtags; (2) we enrich EMBERSim with automatically determined malware class tagsusing the open-source tool AVClass on VirusTotal data and (3) we describe andshare the implementation for our class scoring technique and leaf similaritymethod.

EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis