EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis
This work addresses the lack of comprehensive data for similarity-targeted research in malware analysis, which is crucial for improving detection methods in adversarial environments, though it is incremental as it builds on an existing dataset.
The authors tackled the scarcity of data for similarity research in malware analysis by creating EMBERSim, an augmented dataset based on EMBER that includes similarity-informed tags and malware class tags, enabling further studies in this area.
In recent years there has been a shift from heuristics-based malware detection towards machine learning, which proves to be more robust in the current heavily adversarial threat landscape. While we acknowledge machine learning to be better equipped to mine for patterns in the increasingly high amounts of similar-looking files, we also note a remarkable scarcity of the data available for similarity-targeted research. Moreover, we observe that the focus in the few related works falls on quantifying similarity in malware, often overlooking the clean data. This one-sided quantification is especially dangerous in the context of detection bypass. We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER - one of the largest malware classification data sets. We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space. Our contribution is threefold: (1) we publish EMBERSim, an augmented version of EMBER, that includes similarity-informed tags; (2) we enrich EMBERSim with automatically determined malware class tags using the open-source tool AVClass on VirusTotal data and (3) we describe and share the implementation for our class scoring technique and leaf similarity method.