Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a g...

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Bibliographic Details
Published in:Computers, materials & continua materials & continua, 2020, Vol.62 (2), p.917-927
Main Authors: Chen, Haiwen, Yu, Guang, Liu, Fang, Cai, Zhiping, Liu, Anfeng, Chen, Shuhui, Huang, Hongbin, Fong Cheang, Chak
Format: Article
Language:English
Subjects:
Anomalies
Feature extraction
Supervised learning
Troubleshooting
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Summary:For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need at present. In this paper, unsupervised learning DBSCAN combined with feature extraction of data has been used, and for some KPIs, its best F-Score can reach about 0.9, which is quite good for solving the current problem.
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2020.05981