Anomaly Detection with Machine Learningedit

For Free Trial, Cloud and Platinum License deployments, Machine Learning functionality is available throughout the SIEM app. You can view the details of detected anomalies within the Anomalies table 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 details in the overview of the Host and IP Details pages. Each of these interfaces also offer the ability to drag and drop details of the anomaly to Timeline, such as the Entity itself, or any of the associated Influencers.

ml ui

Manage machine learning jobsedit

For users with the ml_admin role, the Anomaly Detection interface within the main navigation header can be used for for viewing, starting, and stopping SIEM machine learning jobs.

To add a custom job to the Anomaly Detection interface, add a SIEM tag to the job’s Group field (Kibana → Machine learning → Create/Edit job → Job details).

Prebuilt jobsedit

The SIEM app comes with prebuilt machine learning anomaly detection jobs for automatically detecting host and network anomalies. The jobs are displayed in the Anomaly Detection interface. They are available if you ship data using Beats and Kibana is configured with the required index patterns (auditbeat-*, filebeat-*, packetbeat-*, or winlogbeat-* via Kibana → Management → Index Patterns).

Machine learning jobs look back and analyse two weeks of historical data prior to the time they are enabled. After jobs are enabled, they continuously analyse incoming data. When jobs are stopped and restarted within the two week timeframe, previously analysed data is not processed again.

Prebuilt job reference describes all available machine learning jobs and lists which beats are required on your hosts for each job. For information on tuning anomaly results to reduce the number of false positive, see Optimizing anomaly results.

View detected anomaliesedit

To view the Anomalies table widget and Max Anomaly Score By Job details, the user must have the ml_admin or ml_user role.

To adjust the score threshold for which anomalies are shown, you can modify Kibana → Management → Advanced Settings → siem:defaultAnomalyScore.