You are viewing docs on Elastic's new documentation system, currently in technical preview. For all other Elastic docs, visit

Domain Generation Algorithm Detection

ML solution package to detect domain generation algorithm (DGA) activity in your network data.

2.0.1 (View all)
Compatible Kibana version(s)
8.9.0 or higher
Supported Serverless project types

Subscription level

The Domain Generated Algorithm (DGA) Detection package contains assets to detect DGA activity in your network data. This package requires a Platinum subscription. Please ensure that you have a Trial or Platinum level subscription installed on your cluster before proceeding. This package is licensed under Elastic License 2.0.

v2.0.0 and beyond

v2.0.0 of the package introduces breaking changes, namely deprecating detection rules from the package. To continue receiving updates to DGA Detection, we recommend upgrading to v2.0.0 after doing the following:

  • Uninstall existing rules associated with this package: Navigate to Security > Rules and delete the following rules:
    • Machine Learning Detected DGA activity using a known SUNBURST DNS domain
    • Machine Learning Detected a DNS Request Predicted to be a DGA Domain
    • Potential DGA Activity
    • Machine Learning Detected a DNS Request With a High DGA Probability Score

Depending on the version of the package you're using, you might also be able to search for the above rules using the tag DGA

  • Upgrade the DGA package to v2.0.0 using the steps here
  • Install the new rules as described in the Enable detection rules section below

In version 2.0.1, the package ignores data in cold and frozen data tiers to reduce heap memory usage, avoid running on outdated data, and to follow best practices.


To download the assets, click Settings > Install Domain Generated Algorithm Detection assets.

Follow these instructions to ingest data with the ingest pipeline and enrich your indices with inference data. Then use the anomaly detection jobs in this package and associated rules in the Detection Engine, for Domain Generated Algorithm detection. For more detailed information refer to the DGA blog

Set up the ingest pipeline

Once you’ve installed the package you can ingest your data using the ingest pipeline. This will enrich your incoming data with its predictions from the machine learning model.

Add preconfigured anomaly detection jobs

Create a data view for the indices that are enriched by the pipeline.

In Machine Learning > Anomaly Detection, when you create a job, you should see an option to Use preconfigured jobs with a card for DGA. When you select the card, you will see a pre-configured anomaly detection job that you can enable depending on what makes the most sense for your environment. Note this job is only useful for indices that have been enriched by the ingest pipeline.

Enable detection rules

You can also enable detection rules to alert on DGA activity in your environment, based on anomalies flagged by the above ML jobs. As of version 2.0.0 of this package, these rules are available as part of the Detection Engine, and can be found using the tag Use Case: Domain Generated Algorithm Detection. See this documentation for more information on importing and enabling the rules.

Anomaly Detection Jobs

Detects potential DGA (domain generation algorithm) activity that is often used by malware command and control (C2) channels. Looks for a source IP address making DNS requests that have an aggregate high probability of being DGA activity.


Usage in production requires that you have a license key that permits use of machine learning features.


VersionDetailsKibana version(s)


Enhancement View pull request
Add query settings to ignore frozen and cold data tiers

8.9.0 or higher


Enhancement View pull request
Removing detection rules from the package, bumped license and format versions, subscription tier

8.9.0 or higher


Enhancement View pull request
Ensure event.kind is correctly set for pipeline errors

8.0.0 or higher


Enhancement View pull request
Add the Advanced Analytics (UEBA) subcategory

8.0.0 or higher


Enhancement View pull request
Update version number to follow GA format and to improve visibility

8.0.0 or higher


Enhancement View pull request
Added categories and/or subcategories.


Enhancement View pull request
Clean up ML job groups and rule tags, change release to ga, documentation updates


Bug fix View pull request
Add a DGA tag to all rules, fix n-gram generation logic, remove a reference to a non-existent ML job in one of the rules.


Bug fix View pull request
Update DGA integration Readme


Enhancement View pull request
Initial release of the package

On this page