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

Mosquera, Alejandro


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{
  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.5534783", 
  "language": "eng", 
  "title": "amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth", 
  "issued": {
    "date-parts": [
      [
        2021, 
        9, 
        28
      ]
    ]
  }, 
  "abstract": "<p>This paper describes the author&#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&#39; defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.</p>", 
  "author": [
    {
      "family": "Mosquera, Alejandro"
    }
  ], 
  "type": "article", 
  "id": "5534783"
}
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