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amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth

Mosquera, Alejandro


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  <dc:creator>Mosquera, Alejandro</dc:creator>
  <dc:date>2021-09-28</dc:date>
  <dc:description>This paper describes the author's participation in the 3rd edition of the Machine Learning Security Evasion Competition (MLSEC-2021) sponsored by CUJO AI, VM-Ray, MRG-Effitas, Nvidia and Microsoft. As in the previous year the goal was not only developing measures against adversarial attacks on a pre-defined set of malware samples but also finding ways of bypassing other teams' defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.</dc:description>
  <dc:identifier>https://zenodo.org/record/5534783</dc:identifier>
  <dc:identifier>10.5281/zenodo.5534783</dc:identifier>
  <dc:identifier>oai:zenodo.org:5534783</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.5534782</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Adversarial machine learning</dc:subject>
  <dc:subject>Malware detection</dc:subject>
  <dc:subject>MLSEC</dc:subject>
  <dc:subject>Static malware detection</dc:subject>
  <dc:title>amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth</dc:title>
  <dc:type>info:eu-repo/semantics/report</dc:type>
  <dc:type>publication-report</dc:type>
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