Rising demand for security in network services is pushing all network and Internet traffic to be encrypted. DPI techniques no longer suffice to detect anomalies using deterministic tools.
Enter machine learning. ML enables network service providers and enterprises to learn from network traffic patterns to detect anomalies in network performance and QoE.
DART unleashes the full potential of ML to augment streaming network analytics, alert classification, and workflow generation capabilities. DART ML offers more powerful insights because it identifies shifting patterns in huge data sets across multiple dimensions.
The DART ML engine, along with the metadata sensor communicating via the DART high speed software bus, creates a model of the traffic data pattern. It then uses this model to detect anomalies in network performance, application performance, and end users’ QoE.
DART engine quickly identifies top-level issues and their causes and suggests network resolutions, resolving network problems faster.