- Machine Learning: other versions:
- Setup and security
- Getting started with machine learning
- Anomaly detection
- Overview
- Concepts
- Configure anomaly detection
- API quick reference
- Supplied configurations
- Function reference
- Examples
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Customizing detectors with custom rules
- Detecting anomalous categories of data
- Detecting anomalous locations in geographic data
- Performing population analysis
- Altering data in your datafeed with runtime fields
- Adding custom URLs to machine learning results
- Handling delayed data
- Mapping anomalies by location
- Exporting and importing machine learning jobs
- Limitations
- Troubleshooting
- Data frame analytics
A newer version is available. For the latest information, see the
current release documentation.
Logs anomaly detection configurations
editLogs anomaly detection configurations
editThese 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.
- Detects anomalies in the count of log entries by category.
- log_entry_rate
-
- For log entries via the Logs UI.
- Models ingestion rates.
- Detects anomalies in the log entry ingestion rate.
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