@Article{jcs.2020.011169,
AUTHOR = {Ji Qian, Fang Liu, Donghui Li, Xin Jin, Feng Li},
TITLE = {Large-Scale KPI Anomaly Detection Based on Ensemble Learning and  Clustering},
JOURNAL = {Journal of Cyber Security},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {157--166},
URL = {http://www.techscience.com/JCS/v2n4/40713},
ISSN = {2579-0064},
ABSTRACT = {Anomaly detection using KPI (Key Performance Indicator) is critical 
for Internet-based services to maintain high service availability. However, given 
the velocity, volume, and diversified nature of monitoring data, it is difficult to 
obtain enough labelled data to build an accurate anomaly detection model for 
using supervised machine leaning methods. In this paper, we propose an 
automatic and generic transfer learning strategy: Detecting anomalies on a new 
KPI by using pretrained model on existing selected labelled KPI. Our approach, 
called KADT (KPI Anomaly Detection based on Transfer Learning), integrates 
KPI clustering and model pretrained techniques. KPI clustering is used to obtain 
the similarity of different KPI data's distribution, and applied transfer knowledge 
from source dataset to the target dataset by model pretrained technique. In our 
evaluation using real-world KPIs from large Internet-based services, the 
clustering algorithm used to detect various KPI curve pattern achieve the best 
classification effect and accuracy More importantly, further evaluation on 30 
KPIs shows that KADT can significantly reduce the time overhead of the model 
training with little loss of accuracy.},
DOI = {10.32604/jcs.2020.011169}
}