@Article{cmc.2020.05981,
AUTHOR = {Haiwen Chen, Guang Yu, Fang Liu, Zhiping Cai, Anfeng Liu, Shuhui Chen, Hongbin Huang, Chak Fong Cheang},
TITLE = {Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {62},
YEAR = {2020},
NUMBER = {2},
PAGES = {917--927},
URL = {http://www.techscience.com/cmc/v62n2/38285},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.05981}
}