DETECTING METRICS INDICATIVE OF OPERATIONAL CHARACTERISTICS OF NETWORK AND IDENTIFYING AND CONTROLLING BASED ON DETECTED ANOMALIES

摘要

A machine learning anomaly detection system receives a time series of metrics indicative of operational characteristics of a computing system architecture. A distribution of the metrics values is identified and a volume of metrics detected during a current evaluation period is identified. A dynamic anomaly detection threshold is generated, based upon the distribution and the volume of detected metrics. Metric values from the current evaluation period are compared to the dynamic anomaly detection threshold to determine whether the metric values in the current evaluation period are anomalous. If so, an action signal is generated.