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Bayesian Ridge Regression-Based Graph Injection Attack on IIoT
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AI
DOI：10.1109/JESTIE.2025.3583886.png)
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Abstract
En 中文
The systems within the Industrial Internet of Things (IIoT) have complex structures and non-Euclidean data, which are challenging to manage. Due to the advantages of graph neural networks (GNNs) in processing non-Euclidean data and complex topologies, they are capable of handling problems in the context of the IIoT. In this work, the IIoT system is structured into multiple layers to facilitate the management of the system and the use of GNNs. GNNs are taken as node classifiers to analyze the state of each edge server in the IIoT system. However, in reality, adversarial attacks often arise in the IIoT, severely impacting system performance. Therefore, a black-box graph injection attack, Bayesian ridge regression injection attack (BRRIA), is proposed to study the impact of the internal relations on a system and to investigate the vulnerabilities of GNNs. Extensive experiments on two public datasets demonstrate the effectiveness of our attack method. In both experiments targeting specific victim nodes and those attacking a certain category of nodes by targeting critical nodes, BRRIA demonstrates a higher attack accuracy compared to an advanced method. Besides, a synthetic dataset designed to simulate industrial production processes was used to demonstrate the effectiveness of the BRRIA method.
Keywords:
Industrial Internet of Things
Servers
Feature extraction
Artificial intelligence
Wireless communication
Industrial electronics
Classification algorithms
Bayes methods
Training
Approximation algorithms
Adversarial attack
black-box attack
graph injection attack
Industrial Internet of Things (IIoT)
Journal
I
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Papers:
138
Citations:
0