Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers-Reference-Cited by-同舟云学术

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Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Published:2023-10-01 Issue: Volume: Page:4985-4990
ISSN:
Container-title:2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
language:
Short-container-title:

Author:

Yuan Haitao¹,Wang Shen¹,Bi Jing²,Zhang Jia³

Affiliation:

1. School of Automation Science and Electrical Engineering, Beihang University,Beijing,China

2. Beijing University of Technology,Faculty of Information Technology,Beijing,China

3. School of Engineering at Southern, Methodist University,Dept. of Computer Science in Lyle,Dallas,TX,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10391856/10393862/10393988.pdf?arnumber=10393988

Reference22 articles.

1. On Jamming and Black Hole Attacks in Heterogeneous Wireless Networks

2. A novel method for detecting disk filtration attacks via the various machine learning algorithms

3. Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection

4. An Unsupervised Deep Learning Model for Early Network Traffic Anomaly Detection

5. Intelligent Quality of Service Aware Traffic Forwarding for Software-Defined Networking/Open Shortest Path First Hybrid Industrial Internet

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hybrid and Spatiotemporal Detection of Cyberattack Network Traffic in Cloud Data Centers;IEEE Internet of Things Journal;2024-05-15

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