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With the increase in the variety and quantity of malware, there is an urgentneed to speed up the diagnosis and the analysis of malware. Extracting themalware family-related tokens from AV (Anti-Virus) labels, provided by onlineanti-virus engines, paves the way for pre-diagnosing the malware. Automaticallyextract the vital information from AV labels will greatly enhance the detectionability of security enterprises and equip the research ability of securityanalysts. Recent works like AVCLASS and AVCLASS2 try to extract the attributesof malware from AV labels and establish the taxonomy based on expert knowledge.However, due to the uncertain trend of complicated malicious behaviors, thesystem needs the following abilities to face the challenge: preserving vitalsemantics, being expansible, and free from expert knowledge. In this work, wepresent AVMiner, an expansible malware tagging system that can mine the mostvital tokens from AV labels. AVMiner adopts natural language processingtechniques and clustering methods to generate a sequence of tokens withoutexpert knowledge ranked by importance. AVMiner can self-update when new samplescome. Finally, we evaluate AVMiner on over 8,000 samples from well-knowndatasets with manually labeled ground truth, which outperforms previous works.

AVMiner: Expansible and Semantic-Preserving Anti-Virus Labels Mining Method