{
  "affects": {
    "artifacts": [
      {
        "name": "Palo Alto Networks malware detection system",
        "type": "System"
      }
    ],
    "deployer": [
      "Palo Alto Networks malware detection system"
    ],
    "developer": []
  },
  "credit": null,
  "data_type": "AVID",
  "data_version": "0.2",
  "description": {
    "lang": "eng",
    "value": "The Palo Alto Networks Security AI research team tested a deep learning model for malware command and control (C\u0026C) traffic detection in HTTP traffic.\nBased on the publicly available [paper by Le et al.](https://arxiv.org/abs/1802.03162), we built a model that was trained on a similar dataset as our production model and had similar performance.\nThen we crafted adversarial samples, queried the model, and adjusted the adversarial sample accordingly until the model was evaded."
  },
  "impact": {
    "avid": {
      "lifecycle_view": [
        "L02: Data Understanding",
        "L06: Deployment"
      ],
      "risk_domain": [
        "Security"
      ],
      "sep_view": [
        "S0403: Adversarial Example"
      ],
      "taxonomy_version": "0.2"
    }
  },
  "last_modified_date": "2023-03-31",
  "metadata": {
    "vuln_id": "AVID-2023-V001"
  },
  "problemtype": {
    "classof": "ATLAS Case Study",
    "description": {
      "lang": "eng",
      "value": "Evasion of Deep Learning Detector for Malware C\u0026C Traffic"
    },
    "type": "Advisory"
  },
  "published_date": "2023-03-31",
  "references": [
    {
      "label": "Evasion of Deep Learning Detector for Malware C\u0026C Traffic",
      "type": "source",
      "url": "https://atlas.mitre.org/studies/AML.CS0000"
    },
    {
      "label": "Le, Hung, et al. \"URLNet: Learning a URL representation with deep learning for malicious URL detection.\" arXiv preprint arXiv:1802.03162 (2018).",
      "type": "source",
      "url": "https://arxiv.org/abs/1802.03162"
    }
  ],
  "reports": null
}