Use case sources
Source reports and analysis supporting this case are listed below. AITBM interpretation and any indicative scoring are derived from this public evidence.
- AIDEFEND SecureFlow — Botnet Domain Generation Algorithm (DGA) Detection Evasion
- MITRE ATLAS case study AML.CS0001
- 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)
- Degas source code (github.com)
Incident overview
The Palo Alto Networks Security AI research team was able to bypass a Convolutional Neural Network based botnet Domain Generation Algorithm (DGA) detector using a generic domain name mutation technique. It is a generic domain mutation technique which can evade most ML-based DGA detection modules. The generic mutation technique evades most ML-based DGA detection modules and can be used to test the effectiveness and robustness of all DGA detection methods developed by security companies in the industry before they are deployed to the production environment.
Why this record has no ERS
The evaded model was a publicly available open-source CNN DGA detector acquired by the researchers, not the Palo Alto Networks production DGA module named as the ATLAS target, and the technique is presented as generic across most ML-based DGA detectors. No result is tied to a named production system, so the record fails the system and observation tests of the scorability admission test.
Status: Not scorable by design · Classification: Exercise · source date 2026-08-02 · primary evidence date 2020-01-01
SecureFlow attack flow
This record mirrors an AIDEFEND SecureFlow flow (MITRE ATLAS case-study conversion): an ordered attack path with MITRE ATLAS tactic and technique labels and the AIDEFEND controls matched to each step. The flow documents what happened and which controls apply; it does not establish an assessed deployment.
| MITRE ATLAS case study | AML.CS0001 (exercise) |
|---|---|
| Target | Palo Alto Networks ML-based DGA detection module |
| Actor | Palo Alto Networks AI Research Team |
| Primary evidence date | 2020-01-01 |
| SecureFlow record updated | 2026-08-02 |
| AI layers | Predictive ML / computer vision |
| Risk impact | Model evasion / bypass |
| Affected components | CNN-based DGA detector, DGA domain datasets, domain-name mutation technique, botnet command-and-control detection pipeline |
Attack steps
| # | Step | ATLAS tactic / technique |
|---|---|---|
| 1 | Research DGA Detection MethodsDGA detection is a widely used technique to detect botnets in academia and industry. The research team searched for research papers related to DGA detection. | AML.TA0002 AML.T0000 |
| 2 | Acquire CNN DGA Detector and DatasetsThe researchers acquired a publicly available CNN-based DGA detection model and tested it against a well-known DGA generated domain name data sets, which includes ~50 million domain names from 64 botnet DGA families. The CNN-based DGA detection model shows more than 70% detection accuracy on 16 (~25%) botnet DGA families. | AML.TA0003 AML.T0002 |
| 3 | Develop Generic Domain Mutation TechniqueThe researchers developed a generic mutation technique that requires a minimal number of iterations. | AML.TA0003 AML.T0017.000 |
| 4 | Generate Evasive Domain NamesThe researchers used the mutation technique to generate evasive domain names. | AML.TA0001 AML.T0043.001 |
| 5 | Measure Detection-Rate CollapseThe experiment results show that the detection rate of all 16 botnet DGA families drop to less than 25% after only one string is inserted once to the DGA generated domain names. | AML.TA0001 AML.T0042 |
| 6 | Evade DGA Detector for C2The DGA generated domain names mutated with this technique successfully evade the target DGA Detection model, allowing an adversary to continue communication with their Command and Control (https://attack.mitre.org/tactics/TA0011/) servers. | AML.TA0007 AML.T0015 |
Threat references
Summary and attack steps reproduced from AIDEFEND SecureFlow (https://aidefendlabs.com/en/secureflow/?flow=atlas-aml-cs0001-botnet-dga-detection-evasion; AIDEFEND content CC BY 4.0), converted from MITRE ATLAS case study AML.CS0001 (Apache License 2.0; MITRE ATLAS is a trademark of The MITRE Corporation).
AIDEFEND evidence routes
AIDEFEND defences → AITBM sub-metrics
Identifiers are quoted as they appear on the AIDEFEND in Action brief or in the AIDEFEND SecureFlow defense map (audited AIDEFEND dataVersion 2026.10.15); the sub-metric mapping is AITBM's own, from the specification's AIDEFEND tables reconciled at catalogue data version 2026.10.15. AIDEFEND renumbers identifiers between releases, so the data version travels with every mapping and neither side's IDs should be cited without one. A mapping identifies a possible evidence route; a recommendation does not prove that the control was implemented or effective and receives no scoring credit by itself.
| Technique | Defence | Priority | Evidences |
|---|---|---|---|
| AID-M-003.002 | General Predictive Performance & Operational BaseliningExact SecureFlow defense-map identifier (flow atlas-aml-cs0001-botnet-dga-detection-evasion), verified against AIDEFEND dataVersion 2026.10.15; matched to attack step step-5. Presence supplies no score credit. | Candidate evidence route | Ro-2 Fa-2 Fa-4 Tr-1 Tr-2 |
| AID-D-002.002 | Labeled Performance Tracking with Ground Truth Feedback LoopExact SecureFlow defense-map identifier (flow atlas-aml-cs0001-botnet-dga-detection-evasion), verified against AIDEFEND dataVersion 2026.10.15; matched to attack steps step-5, step-6. Presence supplies no score credit. | Candidate evidence route | Ro-2 Fa-1 Fa-2 Tr-2 Cn-7 |
| AID-D-002.001 | Input / Output Distribution Drift MonitoringExact SecureFlow defense-map identifier (flow atlas-aml-cs0001-botnet-dga-detection-evasion), verified against AIDEFEND dataVersion 2026.10.15; matched to attack step step-6. Presence supplies no score credit. | Candidate evidence route | Ro-2 Fa-1 Fa-2 Tr-2 Cn-7 |
| AID-H-014 | Ensemble Methods for RobustnessExact SecureFlow defense-map identifier (flow atlas-aml-cs0001-botnet-dga-detection-evasion), verified against AIDEFEND dataVersion 2026.10.15; matched to attack step step-6. Presence supplies no score credit. | Candidate evidence route | Ro-1 Ro-2 |
| AID-H-007.004 | Evaluation Data Integrity, Sufficiency Assurance & Promotion GovernanceExact SecureFlow defense-map identifier (flow atlas-aml-cs0001-botnet-dga-detection-evasion), verified against AIDEFEND dataVersion 2026.10.15; matched to attack step step-6. Presence supplies no score credit. | Candidate evidence route | Ro-4 |
Related AITBM rubrics
Sources
- AIDEFEND SecureFlow — Botnet Domain Generation Algorithm (DGA) Detection Evasion
- MITRE ATLAS case study AML.CS0001
- 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)
- Degas source code (github.com)