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Elastic Docs ›Machine Learning in the Elastic Stack [7.8] ›Anomaly detection ›Supplied anomaly detection configurations

Logs anomaly detection configurations

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Logs anomaly detection configurations

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These anomaly detection jobs appear by default in the Logs app in Kibana. For more details, see the datafeed and job definitions in the logs_ui_* folders in GitHub.

log_entry_categories_count
  • For log entry categories via the Logs UI.
  • Models the occurrences of log events (partition_field_name is event.dataset).
  • Detects anomalies in count of log entries by category (using the count function).
log_entry_rate
  • For log entries via the Logs UI.
  • Models ingestion rates (partition_field_name is event.dataset).
  • Detects anomalies in the log entry ingestion rate (using the low_count function).
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