Conference paper Open Access

On Deception-Based Protection Against Cryptographic Ransomware

Alper Genç, Ziya; Lenzini, Gabriele; Sgandurra, Daniele


JSON Export

{
  "files": [
    {
      "links": {
        "self": "https://zenodo.org/api/files/8c9d282d-1e6e-44ab-9553-277643bb4476/22-On%20Deception-Based%20Protection%20Against%20Cryptographic%20Ransomware.pdf"
      }, 
      "checksum": "md5:e3bdef0b767959838fb711e6fa5a2cbf", 
      "bucket": "8c9d282d-1e6e-44ab-9553-277643bb4476", 
      "key": "22-On Deception-Based Protection Against Cryptographic Ransomware.pdf", 
      "type": "pdf", 
      "size": 516047
    }
  ], 
  "owners": [
    41483
  ], 
  "doi": "10.1007/978-3-030-22038-9_11", 
  "stats": {
    "version_unique_downloads": 32.0, 
    "unique_views": 122.0, 
    "views": 133.0, 
    "version_views": 133.0, 
    "unique_downloads": 32.0, 
    "version_unique_views": 122.0, 
    "volume": 18061645.0, 
    "version_downloads": 35.0, 
    "downloads": 35.0, 
    "version_volume": 18061645.0
  }, 
  "links": {
    "doi": "https://doi.org/10.1007/978-3-030-22038-9_11", 
    "latest_html": "https://zenodo.org/record/3472437", 
    "bucket": "https://zenodo.org/api/files/8c9d282d-1e6e-44ab-9553-277643bb4476", 
    "badge": "https://zenodo.org/badge/doi/10.1007/978-3-030-22038-9_11.svg", 
    "html": "https://zenodo.org/record/3472437", 
    "latest": "https://zenodo.org/api/records/3472437"
  }, 
  "created": "2019-10-04T08:19:42.278271+00:00", 
  "updated": "2020-01-20T17:43:31.025482+00:00", 
  "conceptrecid": "3472436", 
  "revision": 4, 
  "id": 3472437, 
  "metadata": {
    "access_right_category": "success", 
    "doi": "10.1007/978-3-030-22038-9_11", 
    "description": "<p>In order to detect malicious file system activity, some commercial and academic anti-ransomware solutions implement deception-based techniques, specifically by placing decoy files among user files. While this approach raises the bar against current ransomware, as any access to a decoy file is a sign of malicious activity, the robustness of decoy strategies has not been formally analyzed and fully tested. In this paper, we analyze existing decoy strategies and discuss how they are effective in countering current ransomware by defining a set of metrics to measure their robustness. To demonstrate how ransomware can identify existing deception-based detection strategies, we have implemented a proof-ofconcept anti-decoy ransomware that successfully bypasses decoys by using a decision engine with few rules. Finally, we discuss existing issues in decoy-based strategies and propose practical solutions to mitigate them.</p>", 
    "language": "eng", 
    "title": "On Deception-Based Protection Against Cryptographic Ransomware", 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "relations": {
      "version": [
        {
          "count": 1, 
          "index": 0, 
          "parent": {
            "pid_type": "recid", 
            "pid_value": "3472436"
          }, 
          "is_last": true, 
          "last_child": {
            "pid_type": "recid", 
            "pid_value": "3472437"
          }
        }
      ]
    }, 
    "communities": [
      {
        "id": "futuretpm-h2020"
      }
    ], 
    "grants": [
      {
        "code": "779391", 
        "links": {
          "self": "https://zenodo.org/api/grants/10.13039/501100000780::779391"
        }, 
        "title": "Future Proofing the Connected World: A Quantum-Resistant Trusted Platform Module", 
        "acronym": "FutureTPM", 
        "program": "H2020", 
        "funder": {
          "doi": "10.13039/501100000780", 
          "acronyms": [], 
          "name": "European Commission", 
          "links": {
            "self": "https://zenodo.org/api/funders/10.13039/501100000780"
          }
        }
      }
    ], 
    "keywords": [
      "Ransomware", 
      "Cryptographic", 
      "Malware", 
      "Deception", 
      "Decoy"
    ], 
    "publication_date": "2019-06-06", 
    "creators": [
      {
        "name": "Alper Gen\u00e7, Ziya"
      }, 
      {
        "name": "Lenzini, Gabriele"
      }, 
      {
        "name": "Sgandurra, Daniele"
      }
    ], 
    "meeting": {
      "title": "16th Conference on Detection of Intrusions and Malware & Vulnerability Assessment"
    }, 
    "access_right": "open", 
    "resource_type": {
      "subtype": "conferencepaper", 
      "type": "publication", 
      "title": "Conference paper"
    }
  }
}
133
35
views
downloads
Views 133
Downloads 35
Data volume 18.1 MB
Unique views 122
Unique downloads 32

Share

Cite as