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

Mosquera, Alejandro

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Mosquera, Alejandro</dc:creator>
  <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:subject>Adversarial machine learning</dc:subject>
  <dc:subject>Malware detection</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>
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