September 28, 2021 Report Open Access

Mosquera, Alejandro

JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/6b38b226-9c5d-4eb1-8c5b-e9369dfa085f/mlsec2021.pdf"
      }, 
      "checksum": "md5:880872a772a5b14d38de0adb6064673d", 
      "bucket": "6b38b226-9c5d-4eb1-8c5b-e9369dfa085f", 
      "key": "mlsec2021.pdf", 
      "type": "pdf", 
      "size": 84729
    }
  ], 
  "owners": [
    256367
  ], 
  "doi": "10.5281/zenodo.5534783", 
  "stats": {
    "version_unique_downloads": 186.0, 
    "unique_views": 253.0, 
    "views": 308.0, 
    "version_views": 308.0, 
    "unique_downloads": 186.0, 
    "version_unique_views": 253.0, 
    "volume": 17538903.0, 
    "version_downloads": 207.0, 
    "downloads": 207.0, 
    "version_volume": 17538903.0
  }, 
  "links": {
    "doi": "https://doi.org/10.5281/zenodo.5534783", 
    "conceptdoi": "https://doi.org/10.5281/zenodo.5534782", 
    "bucket": "https://zenodo.org/api/files/6b38b226-9c5d-4eb1-8c5b-e9369dfa085f", 
    "conceptbadge": "https://zenodo.org/badge/doi/10.5281/zenodo.5534782.svg", 
    "html": "https://zenodo.org/record/5534783", 
    "latest_html": "https://zenodo.org/record/5534783", 
    "badge": "https://zenodo.org/badge/doi/10.5281/zenodo.5534783.svg", 
    "latest": "https://zenodo.org/api/records/5534783"
  }, 
  "conceptdoi": "10.5281/zenodo.5534782", 
  "created": "2021-09-28T18:52:23.364956+00:00", 
  "updated": "2021-09-29T01:48:26.685421+00:00", 
  "conceptrecid": "5534782", 
  "revision": 2, 
  "id": 5534783, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.5281/zenodo.5534783", 
    "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>", 
    "language": "eng", 
    "title": "amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "5534782"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "5534783"
          }
        }
      ]
    }, 
    "keywords": [
      "Adversarial machine learning", 
      "Malware detection", 
      "MLSEC", 
      "Static malware detection"
    ], 
    "publication_date": "2021-09-28", 
    "creators": [
      {
        "orcid": "0000-0002-6020-3569", 
        "name": "Mosquera, Alejandro"
      }
    ], 
    "access_right": "open", 
    "resource_type": {
      "subtype": "report", 
      "type": "publication", 
      "title": "Report"
    }, 
    "related_identifiers": [
      {
        "scheme": "doi", 
        "identifier": "10.5281/zenodo.5534782", 
        "relation": "isVersionOf"
      }
    ]
  }
}