# A Hybrid Approach for Smart Alert Generation

**Authors:** Yao Zhao, Sophine Zhang, Zhiyuan Yao  
**arXiv:** [2306.07983](https://arxiv.org/abs/2306.07983)  
**Date:** 2023-06-02  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2306.07983v1/

## Key points

- Anomaly detection is crucial in network management for large-scale networking environments
- Challenges include scalability, data heterogeneity, and generalizability
- A novel hybrid model integrates statistical models with a whitelist mechanism to minimize false positive alerts and improve accuracy
- Statistical models leverage a vast database to identify anomalies in time-series data
- The whitelist component filters out consistently alerted nodes to enhance anomaly detection accuracy

## Summary

, , , , 


Anomaly Detection: A Crucial Role in Network Management

In today's large-scale networking environments, implementing intelligent alert systems poses significant challenges. These challenges include scalability, data heterogeneity, and generalizability. To overcome these obstacles, this study introduces a novel hybrid model for an alert system that integrates statistical models with a whitelist mechanism. This approach aims to minimize false positive alerts and improve the accuracy of anomaly detection. The statistical models utilized in this hybrid system leverage a vast database to identify anomalies in time-series data. Additionally, the whitelist component filters out consistently alerted nodes to further enhance the accuracy of anomaly detection. The effectiveness of this proposed model is demonstrated through validation using qualitative data from customer support cases. Moving forward, the research aims to enhance feature engineering and input data while also incorporating human feedback into the model development process. Developed by authors Yao Zhao, Sophine Zhang, and Zhiyuan Yao, this innovative approach represents a significant advancement in smart alert generation for network management systems.