Version	2.1.2 (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.

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 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In version 2.1.1, the package ignores data in cold and frozen data tiers to reduce heap memory usage, avoid running on outdated data, and to follow best practices.

Configuration

To download the assets, click Settings > Install Living off the Land Attack 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, to detect LotL attacks. For more detailed information refer to this 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.

This pipeline is designed to work with Winlogbeat and Endpoint data.

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 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. Note these jobs are only useful for indices that have been enriched by the ingest pipeline.

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.

Anomaly Detection Jobs

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.

Licensing

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

Changelog

Version	Details	Kibana version(s)
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	—