- Machine Learning: other versions:
- What is Elastic Machine Learning?
- Setup and security
- Anomaly detection
- Finding anomalies
- Tutorial: Getting started with anomaly detection
- Advanced concepts
- API quick reference
- How-tos
- Generating alerts for anomaly detection jobs
- Aggregating data for faster performance
- Altering data in your datafeed with runtime fields
- Customizing detectors with custom rules
- Reverting to a model snapshot
- Detecting anomalous locations in geographic data
- Mapping anomalies by location
- Adding custom URLs to machine learning results
- Anomaly detection jobs from visualizations
- Exporting and importing machine learning jobs
- Resources
- Data frame analytics
- Natural language processing
Appendix A: Apache anomaly detection configurations
editAppendix A: Apache anomaly detection configurations
editThese anomaly detection job wizards appear in Kibana if you use the Apache integration in Fleet or you use Filebeat to ship access logs from your Apache HTTP servers to Elasticsearch. The jobs assume that you use fields and data types from the Elastic Common Schema (ECS).
Apache access logs
editThese anomaly detection jobs find unusual activity in HTTP access logs.
For more details, see the datafeed and job definitions in GitHub. Note that these jobs are available in Kibana only if data exists that matches the query specified in the manifest file.
Name | Description | Job | Datafeed |
---|---|---|---|
low_request_rate_apache |
Detects low request rates. |
||
source_ip_request_rate_apache |
Detects unusual source IPs - high request rates. |
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source_ip_url_count_apache |
Detects unusual source IPs - high distinct count of URLs. |
||
status_code_rate_apache |
Detects unusual status code rates. |
||
visitor_rate_apache |
Detects unusual visitor rates. |
Apache access logs (Filebeat)
editThese legacy anomaly detection jobs find unusual activity in HTTP access logs. For the latest versions, install the Apache integration in Fleet; see Apache access logs.
For more details, see the datafeed and job definitions in GitHub.
These configurations are only available if data exists that matches the recognizer query specified in the manifest file.
Name | Description | Job | Datafeed |
---|---|---|---|
low_request_rate_ecs |
Detects low request rates (ECS). |
||
source_ip_request_rate_ecs |
Detects unusual source IPs - high request rates (ECS). |
||
source_ip_url_count_ecs |
Detect unusual source IPs - high distinct count of URLs (ECS). |
||
status_code_rate_ecs |
Detects unusual status code rates (ECS). |
||
visitor_rate_ecs |
Detects unusual visitor rates (ECS). |
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