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conflcits with rba migration
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.github/workflows/appinspect.yml

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- name: Install Python Dependencies and ContentCTL and Atomic Red Team
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run: |
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pip install contentctl>=4.0.0
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pip install contentctl==v5.0.0-alpha.2
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git clone --depth=1 --single-branch --branch=master https://github.com/redcanaryco/atomic-red-team.git external_repos/atomic-red-team
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git clone --depth=1 --single-branch --branch=master https://github.com/mitre/cti external_repos/cti
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.github/workflows/auto-update.yml

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.github/workflows/build.yml

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- name: Install Python Dependencies and ContentCTL and Atomic Red Team
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run: |
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pip install contentctl>=4.0.0
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pip install contentctl==v5.0.0-alpha.2
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git clone --depth=1 --single-branch --branch=master https://github.com/redcanaryco/atomic-red-team.git external_repos/atomic-red-team
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git clone --depth=1 --single-branch --branch=master https://github.com/mitre/cti external_repos/cti
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.github/workflows/unit-testing.yml

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- name: Install Python Dependencies and ContentCTL
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run: |
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python -m pip install --upgrade pip
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pip install contentctl>=4.0.0
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pip install contentctl==v5.0.0-alpha.2
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# Running contentctl test with a few arguments, before running the command make sure you checkout into the current branch of the pull request. This step only performs unit testing on all the changes against the target-branch. In most cases this target branch will be develop
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# Make sure we check out the PR, even if it actually lives in a fork

baselines/baseline_of_blocked_outbound_traffic_from_aws.yml

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date: '2018-05-07'
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author: Bhavin Patel, Splunk
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type: Baseline
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datamodel: []
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description: This search establishes, on a per-hour basis, the average and the standard
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deviation of the number of outbound connections blocked in your VPC flow logs by
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each source IP address (IP address of your EC2 instances). Also recorded is the
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- action
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- src_ip
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- dest_ip
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security_domain: network

baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml

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date: '2020-09-07'
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author: David Dorsey, Splunk
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type: Baseline
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datamodel:
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- Change
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description: This search is used to build a Machine Learning Toolkit (MLTK) model
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for how many API calls are performed by each user. By default, the search uses the
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last 90 days of data to build the model and the model is rebuilt weekly. The model
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- All_Changes.user
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- All_Changes.status
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security_domain: network
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deployment:
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scheduling:
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cron_schedule: 0 2 * * 0
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earliest_time: -90d@d
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latest_time: -1d@d
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schedule_window: auto
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schedule_window: auto

baselines/baseline_of_cloud_instances_destroyed.yml

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date: '2020-08-25'
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author: David Dorsey, Splunk
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type: Baseline
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datamodel:
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- Change
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description: This search is used to build a Machine Learning Toolkit (MLTK) model
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for how many instances are destroyed in the environment. By default, the search
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uses the last 90 days of data to build the model and the model is rebuilt weekly.
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<= 5, 0, 1) | table _time instances_destroyed, HourOfDay, isWeekend | fit DensityFunction
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instances_destroyed by "HourOfDay,isWeekend" into cloud_excessive_instances_destroyed_v1
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dist=expon show_density=true'
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how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
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how_to_implement: "You must have Enterprise Security 6.0 or later, if not you will
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need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
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installed, along with any required dependencies. Depending on the number of users
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in your environment, you may also need to adjust the value for max_inputs in the
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MLTK settings for the DensityFunction algorithm, then ensure that the search completes
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in a reasonable timeframe. By default, the search builds the model using the past
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30 days of data. You can modify the search window to build the model over a longer
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.
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More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
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re-run this search to rebuild the model with the latest data.\nMore information
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on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
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known_false_positives: none
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references: []
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tags:
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- All_Changes.action
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- All_Changes.status
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- All_Changes.object_category
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security_domain: network
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deployment:
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scheduling:
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cron_schedule: 0 2 * * 0
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earliest_time: -90d@d
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latest_time: -1d@d
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schedule_window: auto
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schedule_window: auto

baselines/baseline_of_cloud_instances_launched.yml

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date: '2020-08-14'
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author: David Dorsey, Splunk
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type: Baseline
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datamodel:
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- Change
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description: This search is used to build a Machine Learning Toolkit (MLTK) model
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for how many instances are created in the environment. By default, the search uses
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the last 90 days of data to build the model and the model is rebuilt weekly. The
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<= 5, 0, 1) | table _time instances_launched, HourOfDay, isWeekend | fit DensityFunction
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instances_launched by "HourOfDay,isWeekend" into cloud_excessive_instances_created_v1
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dist=expon show_density=true'
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how_to_implement: 'You must have Enterprise Security 6.0 or later, if not you will
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how_to_implement: "You must have Enterprise Security 6.0 or later, if not you will
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need to verify that the Machine Learning Toolkit (MLTK) version 4.2 or later is
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installed, along with any required dependencies. Depending on the number of users
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in your environment, you may also need to adjust the value for max_inputs in the
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MLTK settings for the DensityFunction algorithm, then ensure that the search completes
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in a reasonable timeframe. By default, the search builds the model using the past
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90 days of data. You can modify the search window to build the model over a longer
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.
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More information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.'
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re-run this search to rebuild the model with the latest data.\nMore information
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on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`."
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known_false_positives: none
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references: []
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tags:
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- All_Changes.action
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- All_Changes.status
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- All_Changes.object_category
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security_domain: network
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deployment:
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scheduling:
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cron_schedule: 0 2 * * 0
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earliest_time: -90d@d
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latest_time: -1d@d
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schedule_window: auto
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schedule_window: auto

baselines/baseline_of_cloud_security_group_api_calls_per_user.yml

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date: '2020-09-07'
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author: David Dorsey, Splunk
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type: Baseline
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datamodel:
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- Change
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description: This search is used to build a Machine Learning Toolkit (MLTK) model
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for how many API calls for security groups are performed by each user. By default,
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the search uses the last 90 days of data to build the model and the model is rebuilt
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- All_Changes.user
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- All_Changes.status
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- All_Changes.object_category
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security_domain: network
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deployment:
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scheduling:
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cron_schedule: 0 2 * * 0
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earliest_time: -90d@d
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latest_time: -1d@d
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schedule_window: auto
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schedule_window: auto

baselines/baseline_of_command_line_length___mltk.yml

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date: '2019-05-08'
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author: Rico Valdez, Splunk
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type: Baseline
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datamodel: []
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description: This search is used to build a Machine Learning Toolkit (MLTK) model
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to characterize the length of the command lines observed for each user in the environment.
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By default, the search uses the last 30 days of data to build the model. The model
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the past 30 days of data. You can modify the search window to build the model over
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a longer period of time, which may give you better results. You may also want to
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periodically re-run this search to rebuild the model with the latest data. More
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information on the algorithm used in the search can be found at `https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
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information on the algorithm used in the search can be found at
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`https://docs.splunk.com/Documentation/MLApp/4.2.0/User/Algorithms#DensityFunction`.
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known_false_positives: none
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references: []
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tags:
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- Splunk Enterprise
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- Splunk Enterprise Security
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- Splunk Cloud
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required_fields:
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- _time
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- Processes.user
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- Processes.dest
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- Processes.process_name
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- Processes.process
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security_domain: endpoint
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deployment:
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scheduling:

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