A new concatenated Multigraph Neural Network for IoT intrusion detection

Altaf, T; Wang, X; Ni, W; Yu, G; Liu, RP; Braun, R

A new concatenated Multigraph Neural Network for IoT intrusion detection

Altaf, T

Wang, X

Ni, W

Yu, G Liu, RP Braun, R

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Internet of Things (Netherlands), 2023, 22
Issue Date:: 2023-07-01

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Field	Value	Language
dc.contributor.author	Altaf, T https://orcid.org/0000-0002-0467-1210
dc.contributor.author	Wang, X https://orcid.org/0000-0001-9439-6437
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X
dc.contributor.author	Yu, G
dc.contributor.author	Liu, RP
dc.contributor.author	Braun, R https://orcid.org/0000-0002-4153-8769
dc.date.accessioned	2024-01-10T23:09:48Z
dc.date.available	2024-01-10T23:09:48Z
dc.date.issued	2023-07-01
dc.identifier.citation	Internet of Things (Netherlands), 2023, 22
dc.identifier.issn	2543-1536
dc.identifier.issn	2542-6605
dc.identifier.uri	http://hdl.handle.net/10453/174242
dc.description.abstract	The last few years have seen a high volume of sophisticated cyber-attacks leading to financial instability and privacy breaches. This reveals the need for a Network Intrusion Detection System (NIDS) that is more robust, effective, and capable of learning complex relations of network flows. Existing machine learning approaches are barely effective for picking new attacks in IoT traffic, primarily due to treating flows independently. Recently, Graph Neural Networks (GNNs) have gained popularity due to their ability to model underlying topology in the form of nodes and edges. We propose a GNN-based NIDS that maximizes the possibility of involving structural characteristics of normal and malicious network traffic. We construct a multi-edged graph structure that captures full communication between any pair of IoT nodes at the edge level. This novel GNN model combines the benefits of spectral and spatial GNNs tailored to process and learn from complex multigraph-structured information. The proposed GNN model is adjusted to work on multi-edges and multi-dimensional edge features. Extensive experimental analysis using evaluation metrics is carried out on four benchmark datasets, demonstrating the competence of our proposed scheme compared to the state-of-the-art GNN models. Our proposed model shows about 2% to 5% improvement in accuracy, precision, F1 and recall offering less training time and model size in the considered datasets. The proposed model has achieved high accuracy ranging from 98.91% to 99.96%, with precision ranging from 98.86 to 99.96, recall ranging from 98.91% to 99.96%, and F1-score ranging from 98.74% to 99.95% in the four benchmark datasets.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Internet of Things (Netherlands)
dc.relation.isbasedon	10.1016/j.iot.2023.100818
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	46 Information and computing sciences
dc.title	A new concatenated Multigraph Neural Network for IoT intrusion detection
dc.type	Journal Article
utslib.citation.volume	22
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-01-10T23:09:47Z
pubs.publication-status	Published
pubs.volume	22

Abstract:

The last few years have seen a high volume of sophisticated cyber-attacks leading to financial instability and privacy breaches. This reveals the need for a Network Intrusion Detection System (NIDS) that is more robust, effective, and capable of learning complex relations of network flows. Existing machine learning approaches are barely effective for picking new attacks in IoT traffic, primarily due to treating flows independently. Recently, Graph Neural Networks (GNNs) have gained popularity due to their ability to model underlying topology in the form of nodes and edges. We propose a GNN-based NIDS that maximizes the possibility of involving structural characteristics of normal and malicious network traffic. We construct a multi-edged graph structure that captures full communication between any pair of IoT nodes at the edge level. This novel GNN model combines the benefits of spectral and spatial GNNs tailored to process and learn from complex multigraph-structured information. The proposed GNN model is adjusted to work on multi-edges and multi-dimensional edge features. Extensive experimental analysis using evaluation metrics is carried out on four benchmark datasets, demonstrating the competence of our proposed scheme compared to the state-of-the-art GNN models. Our proposed model shows about 2% to 5% improvement in accuracy, precision, F1 and recall offering less training time and model size in the considered datasets. The proposed model has achieved high accuracy ranging from 98.91% to 99.96%, with precision ranging from 98.86 to 99.96, recall ranging from 98.91% to 99.96%, and F1-score ranging from 98.74% to 99.95% in the four benchmark datasets.

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http://hdl.handle.net/10453/174242