Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Network for IoT Intrusion Detection at Scale-Reference-Cited by-同舟云学术

Divide, Conquer, and Coalesce: Meta Parallel Graph Neural Network for IoT Intrusion Detection at Scale

Published:2024-05-13 Issue: Volume:1 Page:1656-1667
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
language:
Short-container-title:

Author:

Ding Hua¹^ORCID,Chen Lixing²^ORCID,Li Shenghong¹^ORCID,Bai Yang³^ORCID,Zhou Pan⁴^ORCID,Qu Zhe⁵^ORCID

Affiliation:

1. School of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

2. Institute of Cyber Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

4. School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China

5. School of Computer Science and Engineering, Central South University, Changsha, Hunan, China

Funder

he National Nature Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645457

Reference37 articles.

1. A Survey on IoT Intrusion Detection: Federated Learning, Game Theory, Social Psychology, and Explainable AI as Future Directions

2. Leemon Baird. 1995. Residual Algorithms: Reinforcement Learning with Function Approximation. In Machine Learning Proceedings 1995. Elsevier, 30--37.

3. Smart cities of the future

4. Fast unfolding of communities in large networks

5. Detection of sinkhole attacks for supporting secure routing on 6LoWPAN for Internet of Things

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