Benchmarking Label Dynamics of VirusTotal Engines

Shuofei Zhu, Ziyi Zhang, Limin Yang, Linhai Song, Gang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

VirusTotal is the largest online anti-malware scanning service. It is widely used by security researchers for labeling malware data or serving as a comparison baseline. However, several important challenges of using VirusTotal are left unaddressed (e.g., whether VirusTotal labels are already stable, when VirusTotal labels can be trusted), severely harming the correctness of research projects depending on VirusTotal. In this paper, we present VTSet, which contains daily VirusTotal labels on more than 14,000 files over one year. VTSet can be used to build and evaluate various tools to tackle the existing challenges and facilitate the usage of VirusTotal. Besides the data, VTSet also provides a demonstration tool to display many measurement results and a query tool to ease the access of its data. A video demonstration of VTSet is located at the following link: https://youtu.be/aSVaUGHxFi4.

Original languageEnglish (US)
Title of host publicationCCS 2020 - Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
PublisherAssociation for Computing Machinery
Pages2081-2083
Number of pages3
ISBN (Electronic)9781450370899
DOIs
StatePublished - Oct 30 2020
Externally publishedYes
Event27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020 - Virtual, Online, United States
Duration: Nov 9 2020Nov 13 2020

Publication series

NameProceedings of the ACM Conference on Computer and Communications Security
ISSN (Print)1543-7221

Conference

Conference27th ACM SIGSAC Conference on Computer and Communications Security, CCS 2020
Country/TerritoryUnited States
CityVirtual, Online
Period11/9/2011/13/20

Keywords

  • dataset
  • malware detection

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

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