Report Open Access

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

Mosquera, Alejandro


JSON-LD (schema.org) Export

{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<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>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "@id": "https://orcid.org/0000-0002-6020-3569", 
      "@type": "Person", 
      "name": "Mosquera, Alejandro"
    }
  ], 
  "headline": "amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2021-09-28", 
  "url": "https://zenodo.org/record/5534783", 
  "keywords": [
    "Adversarial machine learning", 
    "Malware detection", 
    "MLSEC", 
    "Static malware detection"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.5534783", 
  "@id": "https://doi.org/10.5281/zenodo.5534783", 
  "@type": "ScholarlyArticle", 
  "name": "amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth"
}
151
122
views
downloads
All versions This version
Views 151151
Downloads 122122
Data volume 10.3 MB10.3 MB
Unique views 131131
Unique downloads 107107

Share

Cite as