Microsoft Corporation
Anomaly detection and classification in networked systems

Abstract:

Systems and methods are disclosed for detecting and classifying faults in a networked system. An offline training offline storage and training function can store network data and periodically train machine learning models for identifying faults. Machine learning models can include separate detection and classification models. These machine learning models can be periodically provided to an online anomaly detection function and an online anomaly classification function for detecting and classifying anomalies in real time.