Sergey Polzunov
3bdda091e1
* chore: use `docs-dev` instead of `docs` folder * patch version bump * Rollback an incorrect rename * Use exact docs dir in the helper comment * Revert some overeager renamings * Moving `docs` to `docs-dev` * Update Docs Paths --------- Co-authored-by: eric-forte-elastic <eric.forte@elastic.co>
56 lines
3.1 KiB
Markdown
56 lines
3.1 KiB
Markdown
**The setup instructions in this document have been deprecated. Please follow the steps outlined in [this](https://www.elastic.co/security-labs/detect-domain-generation-algorithm-activity-with-new-Kibana-integration) blog to enable DGA detection in your environment.**
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# Machine Learning on Domain Generation Algorithm (DGA)
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To create and use supervised DGA ML models to enrich data within the stack, check out these Elastic blogs:
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* Part 1: [Machine learning in cybersecurity: Training supervised models to detect DGA activity](https://www.elastic.co/blog/machine-learning-in-cybersecurity-training-supervised-models-to-detect-dga-activity)
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* Part 2: [Machine learning in cybersecurity: Detecting DGA activity in network data](https://www.elastic.co/blog/machine-learning-in-cybersecurity-detecting-dga-activity-in-network-data)
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You can also find some supplementary material and examples [here](https://github.com/elastic/examples/tree/master/Machine%20Learning/DGA%20Detection)
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We also released a blog about getting started with DGA using the CLI and Kibana, which also includes a case study of the process applied to the 2020 [SolarWinds supply chain attack](https://www.elastic.co/blog/elastic-security-provides-free-and-open-protections-for-sunburst):
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* [Combining supervised and unsupervised machine learning for DGA detection](https://www.elastic.co/blog/supervised-and-unsupervised-machine-learning-for-dga-detection)
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For questions, please reach out to the ML team in the #machine-learning channel of the
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[Elastic community Slack workspace](https://www.elastic.co/blog/join-our-elastic-stack-workspace-on-slack)
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The team can also be reached by using the `stack-machine-learning` tag in the [discuss forums](https://discuss.elastic.co/tags/c/elastic-stack/stack-machine-learning)
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*Note: in order to use these ML features, you must have a platinum or higher [subscription](https://www.elastic.co/subscriptions)*
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*Note: the ML features are considered experimental in Kibana as well as this rules CLI*
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## Detailed steps
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#### 1. Upload and setup the model file and dependencies
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Run `python -m detection_rules es <args_or_config> experimental ml setup -t <release-tag>`
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*If updating a new model, you should first uninstall any existing models using `remove-model`*
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You can also upload files locally using the `-d` option, so long as the naming convention of the files match the
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expected pattern for the filenames.
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#### 2. Update packetbeat configuration
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You will need to update your packetbeat.yml config file to point to the enrichment pipeline
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Under `Elasticsearch Output` add the following:
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```yaml
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output.elasticsearch:
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hosts: ["your-hostname:your-port"]
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pipeline: dns_enrich_pipeline
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```
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#### 3. Refresh your packetbeat index
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You can optionally choose to refresh your packetbeat index mapping from within Kibana:
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* Navigate to `Stack Management > (Kibana) Index Patterns`
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* Select the appropriate packetbeat index
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* Click `refresh field list`
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#### 4. Verify enrichment fields
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Any packetbeat documents with the field `dns.question.registered_domain` should now be enriched with `ml_is_dga.*`
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