Open Access
ARTICLE
Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements
Haiwen Chen1, Guang Yu1, Fang Liu2, Zhiping Cai1, *, Anfeng Liu3, Shuhui Chen1, Hongbin Huang1, Chak Fong Cheang4
1 College of Computer Science, National University of Defense Technology, Changsha, China.
2 School of Data Science and Computer Science, Sun Yat-sen University, Guangzhou, China.
3 College of Information and Engineering, Central South University, Changsha, China.
4 Faculty of Information Technology, Macau University of Science and Technology, Macau.
* Corresponding Author: Zhiping Cai. Email: .
Computers, Materials & Continua 2020, 62(2), 917-927. https://doi.org/10.32604/cmc.2020.05981
Abstract
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.
Keywords
Cite This Article
APA Style
Chen, H., Yu, G., Liu, F., Cai, Z., Liu, A. et al. (2020). Unsupervised anomaly detection via DBSCAN for kpis jitters in network managements. Computers, Materials & Continua, 62(2), 917-927. https://doi.org/10.32604/cmc.2020.05981
Vancouver Style
Chen H, Yu G, Liu F, Cai Z, Liu A, Chen S, et al. Unsupervised anomaly detection via DBSCAN for kpis jitters in network managements. Comput Mater Contin. 2020;62(2):917-927 https://doi.org/10.32604/cmc.2020.05981
IEEE Style
H. Chen et al., "Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements," Comput. Mater. Contin., vol. 62, no. 2, pp. 917-927. 2020. https://doi.org/10.32604/cmc.2020.05981