AWS IoT Device Defender ML Detect Custom Metrics and Dimensions support
We are excited to introduce two new enhancements to AWS IoT Device Defender ML Detect, Custom Metrics and Dimensions support. ML Detect now supports monitoring of custom metrics, allowing you to evaluate operational health parameters that are unique to your fleet. Besides setting static alarms manually with Rules Detect, you can now use machine learning to automatically learn your fleet's expected behaviors on custom metrics. Further, with the new Dimensions filter support for ML Detect, you can define attributes to evaluate more precise metrics in your ML security profile.
We are excited to introduce two new enhancements to AWS IoT Device Defender ML Detect, Custom Metrics and Dimensions support. ML Detect now supports monitoring of custom metrics, allowing you to evaluate operational health parameters that are unique to your fleet. Besides setting static alarms manually with Rules Detect, you can now use machine learning to automatically learn your fleet's expected behaviors on custom metrics. Further, with the new Dimensions filter support for ML Detect, you can define attributes to evaluate more precise metrics in your ML security profile.
In this release, custom metrics on ML Detect supports the number-type metrics, such as device’s connection signal strength or percentage of CPU usage, while the dimensions feature provides support for MQTT-topic-filter on four cloud-side metrics (number of messages received, message byte size, number of messages sent, and number of authorization failures). To get started with monitoring custom metrics, you can setup a device side agent with our sample agent in Python or use AWS IoT Device SDK in C++. The custom metrics and dimension capabilities are available in all AWS regions where AWS IoT Device Defender ML Detect is available. For more information, refer to AWS IoT Device Defender documentation.