Domain Generation Algorithm Detection

Version	2.0.4 (View all)
Compatible Kibana version(s)	8.9.0 or higher
Supported Serverless project types What’s this?	Security Observability
Subscription level What’s this?	Platinum

The Domain Generation 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.

For more detailed information refer to the following blogs:

Installation

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Upgrading: If upgrading from a version below v2.0.0, see the section v2.0.0 and beyond.
Add the Integration Package: Install the package via Management > Integrations > Add Domain Generation Algorithm Detection. Configure the integration name and agent policy. Click Save and Continue.
Install assets: Install the assets by clicking Settings > Install Domain Generation Algorithm Detection assets.
Configure the pipeline: To configure the pipeline you can use one of the following steps:
- If using Elastic Defend, add a custom pipeline to the data stream. Go to Stack Management > Ingest Pipelines, and check if the pipeline logs-endpoint.events.network@custom exists. image::images/dga/custom-pipeline.png[Component Templates] If it does not exist, you can create it by running the following command in the Dev Console. Be sure to replace <VERSION> with the current package version.
```
PUT _ingest/pipeline/logs-endpoint.events.network@custom
{
  "processors": [
    {
      "pipeline": {
        "name": "<VERSION>-ml_dga_ingest_pipeline",
        "ignore_missing_pipeline": true,
        "ignore_failure": true
      }
    }
  ]
}
```
- If logs-endpoint.events.process@custom already exists, select the three dots next to it and choose Edit. Click Add a processor. Select Pipeline for Processor, enter <VERSION>-ml_dga_ingest_pipeline for name (replacing <VERSION> with the current package version), and check Ignore missing pipeline and Ignore failures for this processor. Select Add Processor.
- If using an Elastic Beat such as Packetbeat, add the ingest pipeline to it by adding a simple configuration setting to packetbeat.yml and skip to the Add preconfigured anomaly detection jobs section in these instructions.
Add the required mappings to the index or component template: Go to Stack Management > Index Management > Component Templates. Templates that can be edited to add custom components will be marked with a @custom suffix. For instance, the custom component template for Elastic Defend network events is logs-endpoint.events.network@custom. Note: Do not attempt to edit the @package template. image::images/dga/component-templates.png[Component Templates]
- If the @custom component template does not exist, you can execute the following command in the Dev Console to create it and then continue to the Rollover section in these instructions.
```
PUT _component_template/{COMPONENT_TEMPLATE_NAME}@custom
{
  "template": {
    "mappings": {
      "properties": {
        "ml_is_dga": {
          "type": "object",
          "properties": {
            "malicious_prediction": {
              "type": "long"
            },
            "malicious_probability": {
              "type": "float"
            }
          }
        }
      }
    }
  }
}
```
- If the @custom component template already exists, you will need to edit it to add mappings for data to be properly enriched. Click the three dots next to it and select Edit. image::images/dga/component-templates-edit.png[Component Templates]
- On the index settings step, add the following. Be sure to change <VERSION> to the current package version.
```
{
  "index": {
    "default_pipeline": "<VERSION>-ml_dga_ingest_pipeline"
  }
}
```
- Proceed to the mappings step in the UI. Click Add Field at the bottom of the page and create an an Object field for ml_is_dga. image::images/dga/field1.png[Component Templates]
- Finally create two properties under ml_is_dga. image::images/dga/field1a.png[Component Templates]
- The first for malicious_prediction of type Long and then for malicious_probability or type Float. image::images/dga/field2.png[Component Templates]
- Your component mappings should look like the following: image::images/dga/fields-complete.png[Component Templates]
- Click Review then Save Component Template.
Rollover Depending on your environment, you may need to rollover in order for these mappings to get picked up. The deault index pattern for Elastic Defend is logs-endpoint.events.network-default.
```
POST INDEX_NAME/_rollover
```
(Optional) Create a data view for your network logs.
Add preconfigured anomaly detection jobs: 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 in Security > Rules, 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.

Domain Generation Detection Detection Rules

In Security > Rules, filtering with the “Use Case: Domain Generation Algorithm Detection” tag

Anomaly Detection Jobs

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Job	Description
dga_high_sum_probability	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.

v2.0.0 and beyond

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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 and after, 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.

Licensing

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

Changelog

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Changelog

Version	Details	Kibana version(s)
2.0.4	Bug fix (View pull request) Add fields for integration package testing	8.9.0 or higher
2.0.3	Bug fix (View pull request) Add mapping instructions	8.9.0 or higher
2.0.2	Enhancement (View pull request) Improve package installation documentation	8.9.0 or higher
2.0.1	Enhancement (View pull request) Add query settings to ignore frozen and cold data tiers	8.9.0 or higher
2.0.0	Enhancement (View pull request) Removing detection rules from the package, bumped license and format versions, subscription tier	8.9.0 or higher
1.1.0	Enhancement (View pull request) Ensure event.kind is correctly set for pipeline errors	8.0.0 or higher
1.0.1	Enhancement (View pull request) Add the Advanced Analytics (UEBA) subcategory	8.0.0 or higher
1.0.0	Enhancement (View pull request) Update version number to follow GA format and to improve visibility	8.0.0 or higher
0.0.5	Enhancement (View pull request) Added categories and/or subcategories.	—
0.0.4	Enhancement (View pull request) Clean up ML job groups and rule tags, change release to `ga`, documentation updates	—
0.0.3	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.	—
0.0.2	Bug fix (View pull request) Update DGA integration Readme	—
0.0.1	Enhancement (View pull request) Initial release of the package	—

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