AVCLASS: A Tool for Massive Malware Labeling

Labeling a malicious executable as a variant of a known family is important for security applications such as triage, lineage, and for building reference datasets in turn used for evaluating malware clustering and training malware classification approaches. Oftentimes, such labeling is based on labels output by antivirus engines. While AV labels are well-known to be inconsistent, there is often no other information available for labeling, thus security analysts keep relying on them. However, current approaches for extracting family information from AV labels are manual and inaccurate. In this work, we describe AVCLASS, an automatic labeling tool that given the AV labels for a, potentially massive, number of samples outputs the most likely family names for each sample. AVCLASS implements novel automatic techniques to address 3 key challenges: normalization, removal of generic tokens, and alias detection. We have evaluated AVCLASS on 10 datasets comprising 8.9 M samples, larger than any dataset used by malware clustering and classification works. AVCLASS leverages labels from any AV engine, e.g., all 99 AV engines seen in VirusTotal, the largest engine set in the literature. AVCLASS's clustering achieves F1 measures up to 93.9 on labeled datasets and clusters are labeled with fine-grained family names commonly used by the AV vendors. We release AVCLASS to the community.