A Hybrid Approach for Smart Alert Generation
This addresses scalability, data heterogeneity, and maintainability issues in network management, but it is incremental.
The paper tackled the challenge of deploying intelligent alert systems in large-scale networking by proposing a hybrid model combining statistical models with a whitelist mechanism to reduce false positive alerts, validated using qualitative customer support data.
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.