{
 "id": "AIR-2020-0033",
 "slug": "botnet-dga-detection-evasion-using-domain-mutation",
 "title": "Botnet DGA Detection Evasion Using Domain Mutation",
 "summary": "Researchers bypassed a convolutional neural network-based botnet domain generation algorithm detector using generic domain name mutation techniques. The study tested the robustness of industry machine learning classifiers against adversarial inputs.",
 "type": "attack_on_ai",
 "categories": [
  "adversarial_evasion"
 ],
 "severity": 1,
 "targets": [
  "enterprise_deployment"
 ],
 "products": [
  "DGA detector"
 ],
 "incident_date": "2020-01-01",
 "incident_date_precision": "day",
 "first_reported": "2020-12-15",
 "sources": [
  {
   "url": "https://atlas.mitre.org/studies/AML.CS0001",
   "publisher": "MITRE ATLAS",
   "title": "Botnet Domain Generation Algorithm (DGA) Detection Evasion",
   "published_at": "2020-12-15"
  },
  {
   "url": "https://faculty.washington.edu/mdecock/papers/byu2018a.pdf",
   "publisher": "faculty.washington.edu",
   "title": "Yu, Bin, Jie Pan, Jiaming Hu, Anderson Nascimento, and Martine De Cock.  \"Character level based detection of DGA domain names.\" In 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1-8. IEEE, 2018.",
   "published_at": "2020-12-15"
  },
  {
   "url": "https://github.com/matthoffman/degas",
   "publisher": "GitHub",
   "title": "Degas source code",
   "published_at": "2020-12-15"
  }
 ],
 "source_count": 3,
 "confidence": 0.95,
 "status": "published",
 "origin": "seed_atlas",
 "tags": [
  "adversarial-evasion",
  "dga",
  "botnet"
 ],
 "created_at": "2026-10-18T06:55:08.239Z",
 "updated_at": "2026-10-18T06:55:08.239Z"
}