- Title
- Layered malicious nodes detection with graph attention network in human-cyber-physical networks
- Creator
- Lin, Yuhang; Huang, Yanze; Hsieh, Sun-Yuan; Lin, Limei; Xia, Feng
- Date
- 2022
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/190406
- Identifier
- vital:17635
- Identifier
-
https://doi.org/10.1109/MASS56207.2022.00079
- Identifier
- ISBN:9781665471800 (ISBN)
- Abstract
- With the advancement of network information technology and smart device technology, cyberspace is gradually evolved into Human-Cyber-Physical Networks (HCPNs). At the same time, the security problems caused by malicious nodes are becoming more and more serious. It is urgent to propose an efficient approach for malicious node detection. In this paper, we apply graph attention network (GAT) to detect malicious nodes layer by layer in HCPN. In addition, we investigate the influence of graph structure features on the detection performance in terms of accuracy, precision, recall, F1-score by comparing with graph convolutional network-based approach. Experimental results show that our approach has better performance as well as stronger generalizability than graph convolutional network-based approach in general. © 2022 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 19th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2022, Dever USA, 20-22 October 2022, Proceedings 2022 IEEE 19th International Conference on Mobile Ad Hoc and Smart Systems MASS 2022 p. 523-529
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2022 IEEE
- Subject
- Graph Attention Network; Human-Cyber-Physical Networks; Malicious Nodes Detection
- Reviewed
- Funder
- National Natural Science Foundation of China Fok Ying Tung Education Foundation Natural Science Foundation of Fujian Providence
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