# A Hybrid Approach for Smart Alert Generation

arXiv: https://arxiv.org/abs/2306.07983 · HTML: https://mlsanity.com/paper/2306.07983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 2 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications](https://mlsanity.com/paper/1802.03903/) (2018)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing](https://mlsanity.com/paper/2006.03712/) (2020)
- [Efficient Data-Driven Network Functions](https://mlsanity.com/paper/2208.11385/) (2022)

## Built on by (most cited first, 1 of 1)

- [Develop End-to-End Anomaly Detection System](https://mlsanity.com/paper/2402.10085/) (2024)

## Abstract (arXiv)

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.