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

Mosquera, Alejandro


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  <identifier identifierType="DOI">10.5281/zenodo.5534783</identifier>
  <creators>
    <creator>
      <creatorName>Mosquera, Alejandro</creatorName>
      <givenName>Alejandro</givenName>
      <familyName>Mosquera</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-6020-3569</nameIdentifier>
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  <titles>
    <title>amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2021</publicationYear>
  <subjects>
    <subject>Adversarial machine learning</subject>
    <subject>Malware detection</subject>
    <subject>MLSEC</subject>
    <subject>Static malware detection</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2021-09-28</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Report"/>
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  </relatedIdentifiers>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;This paper describes the author&amp;#39;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&amp;#39; defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.&lt;/p&gt;</description>
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