AVclass: A Tool for Massive Malware Labeling

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How researchers see this paper

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A Survey of Android Malware Detection with Deep Neural Models (2020, methodology): “The second method is to use AVclass [46] so Android malware samples are labeled by aggregating labels predicted by multiple AV engines.”
Deep Ground Truth Analysis of Current Android Malware (2017, methodology): “This process is very similar to AVclass [25], although we developed the approach independently without the knowledge of the AVclass work.”
You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis (2020, methodology): “These malware samples are classiﬁed into 189 malware families with AVClass [94].”
Prevalence and Impact of Low-Entropy Packing Schemes in the Malware Ecosystem (2020, methodology): “We also downloaded the VirusTotal report of every sample in our dataset and using AVclass [40], a malware labeling tool, and we have identiﬁed the family associated to each sample.”
Robust Intelligent Malware Detection Using Deep Learning (2019, methodology): “11 AVclass [42] tool was used for labeling the malware samples.”
On the effectiveness of system API-related information for Android ransomware detection (2018, methodology): “…samples whose anti-malware label included the word ransom ; (ii) for each downloaded sample, we extracted its family by using the AVClass tool [23], which essentially combines the various labels provided by anti-malware solutions to create a unique label that identiﬁes the sample itself;…”