Detecting Cloud Anomaly via Broad Network-Based Contrastive Autoencoder-Reference-Cited by-同舟云学术

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Detecting Cloud Anomaly via Broad Network-Based Contrastive Autoencoder

Published:2024-06 Issue:3 Volume:21 Page:3249-3263
ISSN:1932-4537
Container-title:IEEE Transactions on Network and Service Management
language:
Short-container-title:IEEE Trans. Netw. Serv. Manage.

Author:

Zhong Guoxiang¹^ORCID,Liu Fagui¹^ORCID,Jiang Jun²^ORCID,Wang Bin³^ORCID,Yao Xi¹,Chen C. L. Philip¹^ORCID

Affiliation:

1. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

2. College of Information Science and Technology, Nanjing Forestry University, Nanjing, China

3. Department of New Networks, Peng Cheng Laboratory, Shenzhen, China

Funder

Guangdong Major Project of Basic and Applied Basic Research

Science and Technology Major Project of Guangzhou

Science and Technology Project of Guangdong Province

Major Key Project of PCL, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/4275028/10599562/10399962.pdf?arnumber=10399962

Reference62 articles.

1. Detecting Performance Anomalies in Cloud Platform Applications

2. A Fault Tolerant Elastic Resource Management Framework Toward High Availability of Cloud Services

3. A Dynamic and Failure-Aware Task Scheduling Framework for Hadoop

4. Network Services Anomalies in NFV: Survey, Taxonomy, and Verification Methods

5. Workflow-Aware Automatic Fault Diagnosis for Microservice-Based Applications With Statistics

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1. FedAnom: Strengthening Cloud Network Security with Federated Anomaly Detection;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

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