---
title: Optimizing anomaly results
description: To gain clearer insights into real threats, you can tune the anomaly results. The following procedures help to reduce the number of false positives: Tune...
url: https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/security/advanced-entity-analytics/optimizing-anomaly-results
products:
  - Elastic Cloud Serverless
  - Elastic Security
---

# Optimizing anomaly results
To gain clearer insights into real threats, you can tune the anomaly results. The following procedures help to reduce the number of false positives:
- [Tune results for rare applications and processes](#rarely-used-processes)
- [Define an anomaly threshold for a job](#define-rule-threshold)


## Filter out anomalies from rarely used applications and processes

When anomalies include results from a known process that only runs occasionally, you can filter out the unwanted results.
For example, to filter out results from a housekeeping process, named `maintenanceservice.exe`, that only executes occasionally you need to:
1. [Create a filter list](#create-fiter-list)
2. [Add the filter to the relevant job](#add-job-filter)
3. [Clone and rerun the job](#clone-job) (optional)


### Create a filter list

1. Find **Machine Learning** in the navigation menu or by using the [global search field](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/explore-analyze/find-and-organize/find-apps-and-objects).
2. Under **Anomaly Detection**, select **Settings**.
3. Click **Filter Lists** and then **New**.
   The **Create new filter list** pane is displayed.
4. Enter a filter list ID.
5. Enter a description for the filter list (optional).
6. Click **Add item**.
7. In the **Items** textbox, enter the name of the process for which you want to filter out anomaly results (`maintenanceservice.exe` in our example).
   ![filter add item](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/images/security-filter-add-item.png)
8. Click **Add** and then **Save**.
   The new filter appears in the Filter List and can be added to relevant jobs.


### Add the filter to the relevant job

1. Find **Machine Learning** in the navigation menu.
2. Under **Anomaly Detection**, select **Anomaly Explorer**.
3. Navigate to the job results for which the filter is required. If the job results are not listed, click **Edit job selection** and select the relevant job.
4. In the **actions** column, click the gear icon and then select *Configure rules*.
   The **Create Rule** window is displayed.
   ![rule scope](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/images/security-rule-scope.png)
5. Select:
   1. *Add a filter list to limit where the rule applies*.
2. The *WHEN* statement for the relevant detector (`process.name` in our example).
3. The *IS IN* statement.
4. The filter you created as part of the [Create a filter list](#create-fiter-list) procedure.
   <tip>
   For more information, see [Customizing detectors with custom rules](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/explore-analyze/machine-learning/anomaly-detection/ml-configuring-detector-custom-rules).
   </tip>
6. Click **Save**.

<note>
  Changes to rules only affect new results. All anomalies found by the job before the filter was added are still displayed.
</note>


### Clone and rerun the job

If you want to remove all the previously detected results for the process, you must clone and run the cloned job.
<important>
  Running the cloned job can take some time. Only run the job after you have completed all job rule changes.
</important>

1. Find **Machine Learning** in the navigation menu.
2. Under **Anomaly Detection**, select **Jobs**.
3. Navigate to the job for which you configured the rule.
4. Optionally, expand the job row and click **JSON** to verify the configured filter appears under `custom rules` in the JSON code.
5. In the **actions** column, click the more (three dots) icon and select *Clone job*.
   The **Configure datafeed** page is displayed.
6. Click **Data Preview** and check the data is displayed without errors.
7. Click **Next** until the **Job details** page is displayed.
8. Enter a Job ID for the cloned job that indicates it is an iteration of the original one. For example, append a number or a username to the original job name, such as `windows-rare-network-process-2`.
   ![cloned job details](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/images/security-cloned-job-details.png)
9. Click **Next** and check the job validates without errors. You can ignore warnings about multiple influencers.
10. Click **Next** and then **Create job**.
   The **Start <job name>** window is displayed.
   ![start job window](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/images/security-start-job-window.png)
11. Select the point of time from which the job will analyze anomalies.
12. Click **Start**.
   After a while, results will start to appear on the **Anomaly Explorer** page.


## Define an anomaly threshold for a job

Certain jobs use a high-count function to look for unusual spikes in process events. For some processes, a burst of activity is a normal, such as automation and housekeeping jobs running on server fleets. However, sometimes a high-delta event count is unlikely to be the result of routine behavior. In these cases, you can define a minimum threshold for when a high-event count is considered an anomaly.
Depending on your anomaly detection results, you may want to set a minimum event count threshold for the `packetbeat_dns_tunneling` job:
1. Find **Machine Learning** in the navigation menu.
2. Under **Anomaly Detection**, select **Anomaly Explorer**.
3. Navigate to the job results for the `packetbeat_dns_tunneling` job. If the job results are not listed, click **Edit job selection** and select `packetbeat_dns_tunneling`.
4. In the **actions** column, click the gear icon and then select *Configure rules*.
   The **Create Rule** window is displayed.
   ![ml rule threshold](https://docs-v3-preview.elastic.dev/elastic/docs-content/pull/4236/solutions/images/security-ml-rule-threshold.png)
5. Select *Add numeric conditions for when the rule applies* and the following `when` statement:
   *WHEN actual IS GREATER THAN <X>*
   Where `<X>` is the threshold above which anomalies are detected.
6. Click **Save**.
7. To apply the new threshold, rerun the job by selecting **Actions** → **Start datafeed** on the **Anomaly Detection Jobs** page.