  Anomaly detection is an important task in network management. However,
deploying intelligent alert systems in real-world large-scale networking
systems is challenging when we take into account (i) scalability, (ii) data
heterogeneity, and (iii) generalizability and maintainability. In this paper,
we propose a hybrid model for an alert system that combines statistical models
with a whitelist mechanism to tackle these challenges and reduce false positive
alerts. The statistical models take advantage of a large database to detect
anomalies in time-series data, while the whitelist filters out persistently
alerted nodes to further reduce false positives. Our model is validated using
qualitative data from customer support cases. Future work includes more feature
engineering and input data, as well as including human feedback in the model
development process.
