Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning-Reference-Cited by-同舟云学术

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Network Intrusion Detection Based on Graph Neural Network and Ensemble Learning

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
language:
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Author:

Zhang Lina¹,Wang Yang¹,Wu Minyue¹,Wang Yanwei²,Zheng Ying¹

Affiliation:

1. Huazhong University of Science and Technology,The Belt and Road Joint Laboratory on Measurement and Control Technology, School of Artificial Intelligence and Automation,Wuhan,China,430074

2. Wuhan Institute of Technology,School of Mechanical & Electrical Engineering,Wuhan,China,430073

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295926.pdf?arnumber=10295926

Reference22 articles.

1. CSE-IDS: Using cost-sensitive deep learning and ensemble algorithms to handle class imbalance in network-based intrusion detection systems

2. EmSM: Ensemble Mixed Sampling Method for Classifying Imbalanced Intrusion Detection Data

3. Intrusion Detection Evaluation Dataset (CICIDS2017),2017

4. Application of a Dynamic Line Graph Neural Network for Intrusion Detection With Semisupervised Learning

5. Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

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