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Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning
2019
IEEE Transactions on Information Forensics and Security
Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal
doi:10.1109/tifs.2019.2947861
fatcat:yps5spdsyresnepfjqi4kz236m