You can view the details of detected anomalies within the
widget shown on the Hosts, Network, and associated details pages, or even narrow
to the specific date range of an anomaly from the
Max anomaly score by job field
in the overview of the details pages for hosts and IPs. These interfaces also
offer the ability to drag and drop details of the anomaly to Timeline, such as
Entity itself, or any of the associated
If you have the
machine_learning_admin role, you can use the ML job settings interface on the Alerts, Rules, and Rule Exceptions pages to view, start, and stop Elastic Security machine learning jobs.
You can also check the status of machine learning detection rules, and start or stop their associated machine learning jobs:
On the Rules page, the Last response column displays the rule's current status. An indicator icon () also appears if a required machine learning job isn't running. Click the icon to list the affected jobs, then click Visit rule details page to investigate to open the rule's details page.
On a rule's details page, check the Definition section to confirm whether the required machine learning jobs are running. Switch the toggles on or off to run or stop each job.
Elastic Security comes with prebuilt machine learning anomaly detection jobs for automatically detecting
host and network anomalies. The jobs are displayed in the
interface. They are available when either:
- You ship data using Beats or the
Elastic Agent, and Kibana is configured with the required index
patterns (such as
winlogbeat-*in Project settings → Management → Index Management).
- Your shipped data is ECS-compliant, and Kibana is configured with the shipped data's index patterns in Project settings → Management → Index Management.
- You install one or more of the Advanced Analytics integrations.
Prebuilt job reference describes all available machine learning jobs and lists which ECS fields are required on your hosts when you are not using Beats or the Elastic Agent to ship your data. For information on tuning anomaly results to reduce the number of false positives, see Optimizing anomaly results.
Machine learning jobs look back and analyze two weeks of historical data prior to the time they are enabled. After jobs are enabled, they continuously analyze incoming data. When jobs are stopped and restarted within the two-week time frame, previously analyzed data is not processed again.
To view the
Anomalies table widget and
Max Anomaly Score By Job details,
the user must have the
To adjust the
score threshold that determines which anomalies are shown,
you can modify the
securitySolution:defaultAnomalyScore advanced setting.
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