Kentik - Network Flow Analytics

K/ML: Machine Learning

Kentik's machine learning engine, K/ML, applies algorithms and other techniques to surface network anomalies that are worth investigating. By automatically baselining normal network behavior and identifying anomalous network activity, K/ML enables automatic alerting and remediation of unusual network events.

The Machine Learning Difference

The Kentik platform includes an advanced machine learning engine that constantly watches data and metric values as they stream into the system, comparing them with prior patterns of behavior and watching for significant departures from the norm.

The ML engine can watch any parameter in the system, whether coming from raw data inputs or created as an enrichment value as part of inbound ingest. ML signatures are applied automatically by the Kentik system, but can also be defined for specific scenarios of interest to any customer or user.

Anomaly Detection

The K/ML component of the Kentik platform proactively watches for network and application performance degradation, traffic composition changes, availability issues, security incidents, and other anomalies that represent departures from normal patterns of network behavior. When anomalies are recognized, notifications are raised via K/Advise and actions taken via K/Automate.

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