{
 "id": "AIR-2020-0032",
 "slug": "evasion-of-deep-learning-detector-for-malware-c-c-traffic",
 "title": "Evasion of Deep Learning Detector for Malware C&C Traffic",
 "summary": "Researchers crafted adversarial HTTP traffic samples to successfully evade a deep learning model designed for malware command and control detection. The exercise highlighted robustness flaws in AI-based network traffic analysis.",
 "type": "attack_on_ai",
 "categories": [
  "adversarial_evasion"
 ],
 "severity": 1,
 "targets": [
  "enterprise_deployment"
 ],
 "products": [
  "Palo Alto Networks malware detection system"
 ],
 "incident_date": "2020-01-01",
 "incident_date_precision": "day",
 "first_reported": "2020-12-15",
 "sources": [
  {
   "url": "https://atlas.mitre.org/studies/AML.CS0000",
   "publisher": "MITRE ATLAS",
   "title": "Evasion of Deep Learning Detector for Malware C&C Traffic",
   "published_at": "2020-12-15"
  },
  {
   "url": "https://arxiv.org/abs/1802.03162",
   "publisher": "arXiv",
   "title": "Le, Hung, et al. \"URLNet: Learning a URL representation with deep learning for malicious URL detection.\" arXiv preprint arXiv:1802.03162 (2018).",
   "published_at": "2020-12-15"
  }
 ],
 "source_count": 2,
 "confidence": 0.95,
 "status": "published",
 "origin": "seed_atlas",
 "tags": [
  "adversarial-evasion",
  "deep-learning",
  "cnc-traffic"
 ],
 "created_at": "2026-10-18T06:55:08.239Z",
 "updated_at": "2026-10-18T06:55:08.239Z"
}