Detection of anomalous computing environment behavior using glucose

A computer-readable storage device comprising stored instructions that, responsive to execution by one or more processors, cause the one or more processor to perform operations comprising:

• receiving glucose measurements collected by wearable glucose monitoring devices and event records associated with the glucose measurements during a first time period and a second time period;
• identifying missing events that are missing from the event records during the first time period and the second time period by processing the glucose measurements using an event engine simulator;
• training an anomaly detection machine learning model based on the identified missing events during the first time period;
• predicting, using the anomaly detection machine learning model, a non-anomalous range of missing events for the second time period;
• determining anomalous behavior for the second time period based on the identified missing events during the second time period and the predicted non-anomalous range of missing events for the second time period;
• and
• outputting the anomalous behavior.

A computing system comprising:

• one or more memories comprising executable instructions;
• one or more processors in data communication with the one or more memories and configured to execute the executable instructions to:
• receive glucose measurements collected by wearable glucose monitoring devices and event records associated with the glucose measurements during a first time period and a second time period;
• identify missing events that are missing from the event records during the first time period and the second time period by processing the glucose measurements using an event engine simulator;
• train an anomaly detection machine learning model based on the identified missing events during the first time period;
• predict, using the anomaly detection machine learning model, a non-anomalous range of missing events for the second time period;
• determine anomalous behavior for the second time period based on the identified missing events during the second time period and the predicted non-anomalous range of missing events for the second time period;
• and
• output the anomalous behavior.