Living off the Land Attack Detection

Version	2.1.5 (View all)
Compatible Kibana version(s)	8.9.0 or higher
Supported Serverless project types What’s this?	Security
Subscription level What’s this?	Platinum
Level of support What’s this?	Elastic

The Living off the Land Attack (LotL) Detection package contains a supervised machine learning model, called ProblemChild and associated assets, which are used to detect living off the land (LotL) activity in your environment. 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 and webinar:

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 Living off the Land Detection. Configure the integration name and agent policy. Click Save and Continue.
Install assets: Install the assets by clicking Settings > Install Living off the Land 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.process@custom exists. image::images/problemchild/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.process@custom
{
  "processors": [
    {
      "pipeline": {
        "name": "<VERSION>-problem_child_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>-problem_child_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 Winlogbeat, add the ingest pipeline to it by adding a simple configuration setting to winlogbeat.yml.
Add the required mappings to the 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 process events is logs-endpoint.events.process@custom. Note: Do not attempt to edit the @package template. image::images/problemchild/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. Be sure to change <VERSION> to the current package version.
```
PUT _component_template/{COMPONENT_TEMPLATE_NAME}@custom
{
  "template": {
    "settings": {
      "index": {
        "default_pipeline": "<VERSION>-problem_child_ingest_pipeline"
      }
    },
    "mappings": {
      "properties": {
        "blocklist_label": {
          "type": "long"
        },
        "problemchild": {
          "type": "object",
          "properties": {
            "prediction": {
              "type": "long"
            },
            "prediction_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/problemchild/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>-problem_child_ingest_pipeline"
  }
}
```
- Proceed to the mappings step in the UI. Click Add Field at the bottom of the page and create a blocklist_label field of type Long: image::images/problemchild/field1.png[Component Templates]
- Then create an Object field for problemchild. image::images/problemchild/field2.png[Component Templates]
- Finally create two properties under ProblemChild. image::images/problemchild/field2a.png[Component Templates]
- The first for prediction of type Long and then for prediction_probability or type Float. image::images/problemchild/field3.png[Component Templates]
- Your component mappings should look like the following: image::images/problemchild/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.process-default.
```
POST INDEX_NAME/_rollover
```
(Optional) Create a data view specificially for your windows process logs (index pattern or data stream name)
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 Living off the Land Attack Detection. When you select the card, you will see several pre-configured anomaly detection jobs that you can enable depending on what makes the most sense for your environment. Warning: if the ingest pipeline hasn’t run for some reason, such as no eligible data has come in yet, or the required mapping has not been added, you won’t be able to see this card yet. If that is the case, try troubleshooting the ingest pipeline, and if any predictions have been populated yet.
Enable detection rules: You can also enable detection rules to alert on LotL 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: Living off the Land Attack 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: Living off the Land Attack Detection” tag

Anomaly Detection Jobs

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Detects potential LotL activity by identifying malicious processes.

Job	Description
problem_child_rare_process_by_host	Looks for a process that has been classified as malicious on a host that does not commonly manifest malicious process activity.
problem_child_high_sum_by_host	Looks for a set of one or more malicious child processes on a single host.
problem_child_rare_process_by_user	Looks for a process that has been classified as malicious where the user context is unusual and does not commonly manifest malicious process activity.
problem_child_rare_process_by_parent	Looks for rare malicious child processes spawned by a parent process.
problem_child_high_sum_by_user	Looks for a set of one or more malicious processes, started by the same user.
problem_child_high_sum_by_parent	Looks for a set of one or more malicious child processes spawned by the same parent process.

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 LotL 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 a Suspicious Windows Event Predicted to be Malicious Activity
- Unusual Process Spawned By a Host
- Suspicious Windows Process Cluster Spawned by a Host
- Machine Learning Detected a Suspicious Windows Event with a High Malicious Probability Score
- Suspicious Windows Process Cluster Spawned by a Parent Process
- Unusual Process Spawned By a User
- Unusual Process Spawned By a Parent Process
- Suspicious Windows Process Cluster Spawned by a User

Depending on the version of the package you’re using, you might also be able to search for the above rules using the tag Living off the Land.

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

In version 2.1.1, 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.

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)
2.1.5	Bug fix (View pull request) Add fields for integration package testing	8.9.0 or higher
2.1.4	Bug fix (View pull request) Add mapping instructions	8.9.0 or higher
2.1.3	Enhancement (View pull request) Improve package installation documentation	8.9.0 or higher
2.1.2	Enhancement (View pull request) Remove "experimental" messaging from docs	8.9.0 or higher
2.1.1	Enhancement (View pull request) Add query settings to ignore frozen and cold data tiers	8.9.0 or higher
2.1.0	Enhancement (View pull request) Add serverless support	8.9.0 or higher
2.0.0	Enhancement (View pull request) Moving detection rules to the `detection-rules` repo, bumped license version, subscription tier	8.9.0 or higher
1.1.2	Enhancement (View pull request) Convert detection rules to EQL	8.0.0 or higher
1.1.1	Bug fix (View pull request) Update blog post link and minor bug fixes	8.0.0 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) Cleaning up ML job groups and rule tags, documentation updates	—
0.0.4	Bug fix (View pull request) Fix the ML jobs query.	—
0.0.3	Bug fix (View pull request) Add a `LotL` tag to all rules, fix a script in the inference pipeline, update ML job configs.	—
0.0.2	Bug fix (View pull request) Update ProblemChild integration Readme	—
0.0.1	Enhancement (View pull request) Initial release of the package	—

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