diff --git a/reference/data-analysis/machine-learning/ootb-ml-jobs-siem.md b/reference/data-analysis/machine-learning/ootb-ml-jobs-siem.md
index fbf67c32bb..5819e88ece 100644
--- a/reference/data-analysis/machine-learning/ootb-ml-jobs-siem.md
+++ b/reference/data-analysis/machine-learning/ootb-ml-jobs-siem.md
@@ -7,7 +7,6 @@ mapped_pages:
 
 These {{anomaly-jobs}} automatically detect file system and network anomalies on your hosts. They appear in the **Anomaly Detection** interface of the {{security-app}} in {{kib}} when you have data that matches their configuration. For more information, refer to [Anomaly detection with machine learning](/solutions/security/advanced-entity-analytics/anomaly-detection.md).
 
-
 ## Security: Authentication [security-authentication]
 
 Detect anomalous activity in your ECS-compatible authentication logs.
@@ -26,7 +25,6 @@ By default, when you create these job in the {{security-app}}, it uses a {{data-
 | auth_rare_user | Looks for an unusual user name in the authentication logs. An unusual user name is one way of detecting credentialed access by means of a new or dormant user account. A user account that is normally inactive, because the user has left the organization, which becomes active, may be due to credentialed access using a compromised account password. Threat actors will sometimes also create new users as a means of persisting in a compromised web application. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_auth/ml/auth_rare_user.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_auth/ml/datafeed_auth_rare_user.json) |
 | suspicious_login_activity | Detect unusually high number of authentication attempts. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_auth/ml/datafeed_suspicious_login_activity.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_auth/ml/suspicious_login_activity.json) |
 
-
 ## Security: CloudTrail [security-cloudtrail-jobs]
 
 Detect suspicious activity recorded in your CloudTrail logs.
@@ -41,21 +39,19 @@ In the {{ml-app}} app, these configurations are available only when data exists
 | rare_method_for_a_country | Looks for AWS API calls that, while not inherently suspicious or abnormal, are sourcing from a geolocation (country) that is unusual. This can be the result of compromised credentials or keys. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_cloudtrail/ml/rare_method_for_a_country.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_cloudtrail/ml/datafeed_rare_method_for_a_country.json) |
 | rare_method_for_a_username | Looks for AWS API calls that, while not inherently suspicious or abnormal, are sourcing from a user context that does not normally call the method. This can be the result of compromised credentials or keys as someone uses a valid account to persist, move laterally, or exfil data. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_cloudtrail/ml/rare_method_for_a_username.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_cloudtrail/ml/datafeed_rare_method_for_a_username.json) |
 
-
 ## Security: Host [security-host-jobs]
 
 Anomaly detection jobs for host-based threat hunting and detection.
 
 In the {{ml-app}} app, these configurations are available only when data exists that matches the query specified in the [manifest file](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/security_host/manifest.json). In the {{security-app}}, it looks in the {{data-source}} specified in the [`securitySolution:defaultIndex` advanced setting](kibana://reference/advanced-settings.md#securitysolution-defaultindex) for data that matches the query.
 
-To access the host traffic anomalies dashboard in Kibana, go to: `Security -> Dashboards -> Host Traffic Anomalies`.
+To access the host traffic anomalies dashboard in Kibana, install the `Host Traffic Anomalies` integration by navigating to `Management -> Integrations`. Follow the instructions on the integration's `Overview` page to complete the installation. Once the dashboard is successfully installed and configured, you can find it under `Security -> Dashboards -> Host Traffic Anomalies`.
 
 | Name | Description | Job (JSON) | Datafeed |
 | --- | --- | --- | --- |
 | high_count_events_for_a_host_name | Looks for a sudden spike in host based traffic. This can be due to a range of security issues, such as a compromised system, DDoS attacks, malware infections, privilege escalation, or data exfiltration. | [code](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/security_host/ml/high_count_events_for_a_host_name.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/security_host/ml/datafeed_high_count_events_for_a_host_name.json) |
 | low_count_events_for_a_host_name | Looks for a sudden drop in host based traffic. This can be due to a range of security issues, such as a compromised system, a failed service, or a network misconfiguration. | [code](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/security_host/ml/low_count_events_for_a_host_name.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/platform/plugins/shared/ml/server/models/data_recognizer/modules/security_host/ml/datafeed_low_count_events_for_a_host_name.json) |
 
-
 ## Security: Linux [security-linux-jobs]
 
 Anomaly detection jobs for Linux host-based threat hunting and detection.
@@ -79,7 +75,6 @@ In the {{ml-app}} app, these configurations are available only when data exists
 | v3_linux_system_user_discovery | Looks for commands related to system user or owner discovery from an unusual user context. This can be due to uncommon troubleshooting activity or due to a compromised account. A compromised account may be used to engage in system owner or user discovery to identify currently active or primary users of a system. This may be a precursor to additional discovery, credential dumping, or privilege elevation activity. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_linux/ml/v3_linux_system_user_discovery.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_linux/ml/datafeed_v3_linux_system_user_discovery.json) |
 | v3_rare_process_by_host_linux | Looks for processes that are unusual to a particular Linux host. Such unusual processes may indicate unauthorized software, malware, or persistence mechanisms. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_linux/ml/v3_rare_process_by_host_linux.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_linux/ml/datafeed_v3_rare_process_by_host_linux.json) |
 
-
 ## Security: Network [security-network-jobs]
 
 Detect anomalous network activity in your ECS-compatible network logs.
@@ -95,7 +90,6 @@ By default, when you create these jobs in the {{security-app}}, it uses a {{data
 | high_count_network_events | Looks for an unusually large spike in network traffic. Such a burst of traffic, if not caused by a surge in business activity, can be due to suspicious or malicious activity. Large-scale data exfiltration may produce a burst of network traffic; this could also be due to unusually large amounts of reconnaissance or enumeration traffic.  Denial-of-service attacks or traffic floods may also produce such a surge in traffic. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_network/ml/high_count_network_events.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_network/ml/datafeed_high_count_network_events.json) |
 | rare_destination_country | Looks for an unusual destination country name in the network logs. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from a server in a country which does not normally appear in network traffic or business work-flows. Malware instances and persistence mechanisms may communicate with command-and-control (C2) infrastructure in their country of origin, which may be an unusual destination country for the source network. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_network/ml/rare_destination_country.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_network/ml/datafeed_rare_destination_country.json) |
 
-
 ## Security: {{packetbeat}} [security-packetbeat-jobs]
 
 Detect suspicious network activity in {{packetbeat}} data.
@@ -110,7 +104,6 @@ In the {{ml-app}} app, these configurations are available only when data exists
 | packetbeat_rare_urls | Looks for unusual web browsing URL activity that could indicate execution, persistence, command-and-control or data exfiltration activity. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_packetbeat/ml/packetbeat_rare_urls.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_packetbeat/ml/datafeed_packetbeat_rare_urls.json) |
 | packetbeat_rare_user_agent | Looks for unusual HTTP user agent activity that could indicate execution, persistence, command-and-control or data exfiltration activity. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_packetbeat/ml/packetbeat_rare_user_agent.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/siem_packetbeat/ml/datafeed_packetbeat_rare_user_agent.json) |
 
-
 ## Security: Windows [security-windows-jobs]
 
 Anomaly detection jobs for Windows host-based threat hunting and detection.
@@ -134,7 +127,6 @@ If there are additional requirements such as installing the Windows System Monit
 | v3_windows_rare_user_runas_event | Unusual user context switches can be due to privilege escalation. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_windows/ml/v3_windows_rare_user_runas_event.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_windows/ml/datafeed_v3_windows_rare_user_runas_event.json) |
 | v3_windows_rare_user_type10_remote_login | Unusual RDP (remote desktop protocol) user logins can indicate account takeover or credentialed access. | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_windows/ml/v3_windows_rare_user_type10_remote_login.json) | [code](https://github.com/elastic/kibana/blob/master/x-pack/plugins/ml/server/models/data_recognizer/modules/security_windows/ml/datafeed_v3_windows_rare_user_type10_remote_login.json) |
 
-
 ## Security: Elastic Integrations [security-integrations-jobs]
 
 [Elastic Integrations](kibana://reference/index.md) are a streamlined way to add Elastic assets to your environment, such as data ingestion, {{transforms}}, and in this case, {{ml}} capabilities for Security.
@@ -214,4 +206,3 @@ To download, refer to the [documentation](integration-docs://reference/lmd.md).
 | lmd_high_mean_rdp_process_args | Detects unusually high number of process arguments in an RDP session. |
 
 The job configurations and datafeeds can be found [here](https://github.com/elastic/integrations/blob/main/packages/lmd/kibana/ml_module/lmd-ml.json).
-