Machine learning for anomaly detection (demo with BGP prefixes received) - ksator/junos_monitoring_with_healthbot GitHub Wiki

HealthBot and machine learning

HealthBot supports machine learnings for anomaly detection and for outlier detection.

HealthBot supports the following machine learning algorithms for anomaly detection:

• Three-sigma rule
• k-means for anomaly detection

HealthBot supports the following machine learning algorithms for outlier detection:

• DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
• K-fold Three-sigma ("K-Fold Cross-Validation" using "Three-sigma")

Anomaly detection and outlier detection are both about detecting anomalies.
In HealthBot terminology:

• anomaly detection is time based. It compares new data points from a device vs data points collected from the same device during a learning period.
• outlier detection is group based. It analyzes data from a device during a learning Period vs data from other devices during the same learning period

Machine learning demo

Please refer to this other repository to see:

• machine learning 101
• machine learning demo with HealthBot and Junos