gcdRadar
archive/Research/AIR-2020-0033
closedclosed 29 Dec 2020Research1 Research / proof of conceptAI

Botnet DGA Detection Evasion Using Domain Mutation

DGA detector

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.

first report
15 Dec 2020
last update
15 Dec 2020

Timeline · 3

  1. 15 Dec 2020Botnet Domain Generation Algorithm (DGA) Detection Evasion
  2. 15 Dec 2020Yu, 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.
  3. 15 Dec 2020Degas source code

Sources · 3

News and write-ups

  1. 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
  2. 2020-12-15
    Degas source code
    GitHub · github.com/matthoffman/degas

Research and catalogues

  1. 2020-12-15
    Botnet Domain Generation Algorithm (DGA) Detection Evasion
    MITRE ATLAS · atlas.mitre.org/studies/AML.CS0001

Titles and links belong to their publishers. The summary above is written by gcdRadar in its own words; data is CC BY-SA 4.0.

Classification

lane
Research
AI role
Attack on AI
categories
Adversarial evasion
severity
1 · Research / proof of concept
targets
Enterprise deployment
owasp llm top 10
no entry for these categories
mitre atlas
AML.T0015 Evade AI ModelAML.T0043 Craft Adversarial Data
tags
adversarial-evasiondgabotnet
dates
happened 1 Jan 2020 · first reported 15 Dec 2020
origin
MITRE ATLAS
Derived from MITRE ATLAS (Apache 2.0, © MITRE).
record
confidence 0.95 · created 2026-10-18 06:55Z · updated 2026-10-18 06:55Z

Lane, AI role, categories and severity come from the classifier; the OWASP and ATLAS rows are derived from the categories. How these are assigned

Copy a brief

This record as a short Markdown brief, for a ticket, a chat or a coding agent.

the brief as text
# Botnet DGA Detection Evasion Using Domain Mutation

https://gcdradar.vercel.app/incidents/botnet-dga-detection-evasion-using-domain-mutation · AIR-2020-0033 · closed 2020-12-29

- Lane: Research
- Severity: 1 (Research / proof of concept)
- Fix status: Fix status unknown
- First reported: 2020-12-15 (happened 2020-01-01)
- AI role: Attack on AI
- Products: DGA detector

## 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.

## What to do

No action recorded; read the sources below and the vendor's advisory.

## Latest updates

- 2020-12-15: Degas source code (https://github.com/matthoffman/degas)
- 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. (https://faculty.washington.edu/mdecock/papers/byu2018a.pdf)
- 2020-12-15: Botnet Domain Generation Algorithm (DGA) Detection Evasion (https://atlas.mitre.org/studies/AML.CS0001)

## Sources

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

Verify against the vendor's advisory before changing production. Data CC BY-SA 4.0, https://gcdradar.vercel.app.

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