Efficient KPI Anomaly Detection Through Transfer Learning for Large-Scale Web Services

Author:

Zhang Shenglin1ORCID,Zhong Zhenyu2,Li Dongwen2,Fan Qiliang2ORCID,Sun Yongqian2ORCID,Zhu Man2,Zhang Yuzhi1ORCID,Pei Dan3ORCID,Sun Jiyan4,Liu Yinlong4ORCID,Yang Hui5,Zou Yongqiang5

Affiliation:

1. College of Software, Nankai University, Tianjin, China

2. Nankai University, Tianjin, China

3. Department of Computer Science, Tsinghua University, Beijing, China

4. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

5. Accumulus Technologies (Tianjin) Company Ltd., Tianjin, China

Funder

National Key Research and Development Program of China

National Science Foundation of Tianjin

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Beijing National Research Center for Information Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Reference66 articles.

1. FUNNEL: Assessing Software Changes in Web-Based Services

2. A Novel Web Anomaly Detection Approach Based on Semantic Structure

3. Automatic unsupervised outlier model selection;zhao;Proc Adv Neural Inf Process Syst,2021

4. Supervised representation learning: Transfer learning with deep autoencoders;zhuang;Proc Intern Joint Conf Artificial Intel (IJCAI),2015

5. A Comprehensive Survey on Transfer Learning

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