Abstract: A method implemented in a network system to provide anomaly detection simultaneously across elements along end-to-end provisioning flows. The method includes receiving a provisioning log captured from provisioning flows over a predefined period, wherein the provisioning log comprises operations and responses associated with provisioning flow elements comprising transaction types, subscriber segments, network provisioning catalogs (NPCs), and network elements (NEs); determining, based on the provisioning log, metrics, each indicating a success rate for a respective one of the provisioning flow elements; determining a machine learning (ML) model to approximate a probability density of the metrics; determining an anomaly at one or more of the provisioning flow elements based on the ML model; and triggering an action address to the detected anomaly.

Abstract: A method implemented in a network system to provide anomaly detection simultaneously across elements along end-to-end provisioning flows. The method includes receiving a provisioning log captured from provisioning flows over a predefined period, wherein the provisioning log comprises operations and responses associated with provisioning flow elements comprising transaction types, subscriber segments, network provisioning catalogs (NPCs), and network elements (NEs); determining, based on the provisioning log, metrics, each indicating a success rate for a respective one of the provisioning flow elements; determining a machine learning (ML) model to approximate a probability density of the metrics; determining an anomaly at one or more of the provisioning flow elements based on the ML model; and triggering an action address to the detected anomaly.