Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction

Ngo, Tien; Yin, Jiao; Ge, Yong-Feng; Wang, Hua

doi:10.3390/info16060499

Cited by 37Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdversarial Robustness in Machine Learning

Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction

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Ngo, Tien, Yin, Jiao, Ge, Yong-Feng, Wang, Hua·2025·Information

DOI 10.3390/info16060499

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Abstract

The inherent complexity and heterogeneity of the Internet of Things (IoT) ecosystem present significant challenges for developing effective intrusion detection systems. While graph deep-learning-based methods have shown promise in cybersecurity applications, existing approaches primarily construct graphs based on physical network connections, which may not effectively capture node representations. This paper proposes a Top-K Similarity Graph Framework (TKSGF) for IoT network intrusion detection. Instead of relying on physical links, the TKSGF constructs graphs based on Top-K attribute similarity, ensuring a more meaningful representation of node relationships. We employ GraphSAGE as the Graph Neural Network (GNN) model to effectively capture node representations while maintaining scalability. Furthermore, we conducted extensive experiments to analyze the impact of graph directionality (directed vs. undirected), different K values, and various GNN architectures and configurations on detection performance. Evaluations on binary and multi-class classification tasks using the NF-ToN IoT and NF-BoT IoT datasets from the Machine-Learning-Based Network Intrusion Detection System (NIDS) benchmark demonstrated that our proposed framework consistently outperformed traditional machine learning methods and existing graph-based approaches, achieving superior classification accuracy and robustness.

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Detectors that achieve >99% accuracy on a single dataset routinely lose 10-20 percentage points when transferred cross-dataset.
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Lo, W. W., Layeghy, S., Sarhan, M., Gallagher, M. R., & Portmann, M. (2022) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms54207.2022.9789878 · Cited by 463 · see citing works
MGNN-IDS: a multi-graph neural network approach for robust intrusion detection in the internet of things
Alnazzawi, N., Asiri, F., Alturki, N. M., Zhumabayeva, L., Zafar, S., Latif, S., & Ahmad, J. E. (2025) · Telecommunication Systems · doi:10.1007/s11235-025-01352-5 · Cited by 3 · see citing works

Authors: Ngo, T., Yin, J., Ge, Y.-F., & Wang, H.
Affiliations: Victoria University (AU)
Corresponding author: Yin, Jiao
ORCID: Ngo, Tien
Yin, Jiao
Ge, Yong-Feng
Wang, Hua
Venue: Information
Publisher: MDPI AG
Published: 2025-06-16
Volume: 16
Issue: 6
Pages: 499
ISSN: 2078-2489 (ISSN-L)
DOI: 10.3390/info16060499
OpenAlex: W4411345181
Cited by: 37
References: 51
Citations by year: 2026: 27 · 2025: 10
FWCI: 15.53
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Information (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
Physical Sciences › Computer Science › Artificial Intelligence › Internet Traffic Analysis and Secure E-voting
Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
Keywords: IoT intrusion detection, graph neural networks, GraphSAGE, network intrusion detection, node representation learning

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Optimizing IoT Intrusion Detection—A Graph Neural Network Approach with Attribute-Based Graph Construction

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