GCD AI RADAR
incidents/AIR-2020-0033

Botnet DGA Detection Evasion Using Domain Mutation

Attack on AI1 Research / proof of conceptAdversarial evasion

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.

Details

type
Attack on AI
An AI model, agent or AI-powered product was the thing exploited, abused or leaked.
categories
  • Adversarial evasion
    Inputs crafted to make a model misclassify or miss detections (perturbations, evasion of AI-based defences).
owasp llm top 10
no entry for these categories
mitre atlas
Both rows are derived from the type and categories (OWASP 2025 edition, ATLAS release 2026.09), not classified per record. How
severity
1 · Research / proof of concept
Demonstrated by researchers; no known real-world exploitation.
targets
Enterprise deployment
products
DGA detector
incident date
1 Jan 2020
first reported
15 Dec 2020
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
classifier confidence
0.95 · published at or above 0.80; see the methodology
tags
adversarial-evasion · dga · botnet
record
created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z · json

Sources · 3

  1. 2020-12-15
    Botnet Domain Generation Algorithm (DGA) Detection Evasion
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0001
  2. 2020-12-15
    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.
    faculty.washington.edu · faculty.washington.edu/mdecock/papers/byu2018a.pdf
  3. 2020-12-15
    Degas source code
    GitHub · github.com/matthoffman/degas

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