Kaspersky Machine Learning for Anomaly Detection
- About Kaspersky Machine Learning for Anomaly Detection
- What's new
- Basic concepts of Kaspersky MLAD
- Kaspersky MLAD components
- Common deployment scenarios
- Telemetry and event data flow diagram
- Administering Kaspersky MLAD
- Installing the application
- Updating the application and rolling back to the previous installed version
- Getting started
- Starting and stopping Kaspersky MLAD
- Updating Kaspersky MLAD certificates
- First startup of Kaspersky MLAD
- Configuring Kaspersky MLAD
- Configuring the main settings of Kaspersky MLAD
- Configuring the Anomaly Detector service
- Configuring the Keeper service
- Configuring the Mail Notifier service
- Configuring the Similar Anomaly service
- Configuring the Stream Processor service
- Configuring the HTTP Connector
- Configuring the MQTT Connector
- Configuring the AMQP Connector
- Configuring the OPC UA Connector
- Configuring the KICS Connector
- Configuring the CEF Connector
- Configuring the WebSocket Connector
- Configuring the Event Processor service
- Configuring the statuses and causes of incidents
- Configuring logging of Kaspersky MLAD services
- Configuring time intervals for displaying data
- Configuring how the Kaspersky MLAD main menu is displayed
- Exporting and importing a configuration file for Kaspersky MLAD components
- Starting, stopping, and restarting services
- Managing tags
- Managing ML models and templates
- Configuring settings in the Event Processor section
- Managing user accounts
- Managing incident notifications
- Removing the application
- Connecting to Kaspersky MLAD and closing the session
- Kaspersky MLAD web interface
- Licensing the application
- Processing and storing data in Kaspersky MLAD
- Performing common tasks
- Scenario: Working with Kaspersky MLAD
- Viewing summary data in the Dashboard section
- Viewing incoming data in the Monitoring section
- Viewing data in the History section
- Viewing data in the Time slice section
- Viewing data for a specific preset in the Time slice section
- Selecting a specific branch of the ML model in the Time slice section
- Selecting a date and time interval in the Time slice section
- Navigating through time in the Time slice section
- Configuring how graphs are displayed in the Time slice section
- Working with events and patterns
- Working with incidents and groups of incidents
- Scenario: Analysis of incidents
- Viewing incidents
- Viewing the technical specifications of a registered incident
- Viewing incident groups
- Studying the behavior of the monitored asset at the moment when an incident was detected
- Adding a status, cause, expert opinion or note to an incident or incident group
- Exporting incidents to a file
- Working with ML models and templates
- Managing presets
- Viewing the status of a service
- Troubleshooting
- When connecting to Kaspersky MLAD, the browser displays a certificate warning
- The hard drive has run out of free space
- The operating system restarted unexpectedly
- Cannot connect to the Kaspersky MLAD web interface
- Graphs are not displayed in the History and Monitoring sections
- Events are not transmitted between Kaspersky MLAD and external systems
- Cannot load data to view in the Event Processor section
- Data is incorrectly processed in the Event Processor section
- Events are not displayed in the Event Processor section
- Previously created monitors and the specified attention settings are not displayed in the Event Processor section
- The localization language for Help needs to be changed before connecting to the application
- Contacting Technical Support
- Appendix
- Glossary
- Information about third-party code
- Trademark notices
Administering Kaspersky MLAD > Managing ML models and templates > Creating an ML model based on a template
Creating an ML model based on a template
Creating an ML model based on a template
You can create a new ML model based on available templates. When creating an ML model, you can specify the IDs of tags that should be used in the new ML model.
To create an ML model based on a template:
- In the main menu, select the Models section and click Templates.
- In the Action column, click the Create model button in the row of the template you want to use as the basis for creating the ML model.
The Creating a model pane opens on the right.
- Enter a name for the new ML model in the Model name field.
The ML model name must not be longer than 100 characters.
- In the Model tag ID column, select the tag IDs for each tag of the created ML model.
Template tags and tags in the Settings → Tags section are mapped based on the IDs of the ML model tags.
- Click the Create button.
The newly created ML model is displayed on the Models tab. The state of the created ML model will match the training state of the source ML model when the template was created.
Article ID: 238837, Last review: Dec 7, 2022