Ji Qian1, Fang Liu2,*, Donghui Li3, Xin Jin4, Feng Li4
Journal of Cyber Security, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jcs.2020.011169
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… More >
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