- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Advanced concepts
- API quick reference
- Examples
- Tutorial: Getting started with anomaly detection
- 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
- Resources
- Data frame analytics
- Natural language processing
IMPORTANT: No additional bug fixes or documentation updates
will be released for this version. For the latest information, see the
current release documentation.
Appendix D: Logs anomaly detection configurations
editAppendix D: Logs anomaly detection configurations
editThese anomaly detection jobs appear by default in the Logs app in Kibana. For more information about their usage, refer to Categorize log entries and Inspect log anomalies.
Log analysis
editDetect anomalies in log entries via the Logs UI.
| Name | Description | Job | Datafeed |
|---|---|---|---|
log_entry_rate |
Detects anomalies in the log entry ingestion rate |
|
|
Log entry categories
editDetect anomalies in count of log entries by category.
| Name | Description | Job | Datafeed |
|---|---|---|---|
log_entry_categories_count |
Detects anomalies in count of log entries by category |
|
|
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