Resilient Topology Reconfiguration for Industrial Internet of Things: A Feature-Driven Approach Against Heterogeneous Attacks

This paper proposes a feature-driven topology reconfiguration framework to enhance the resilience of Industrial Internet of Things (IIoT) systems against heterogeneous attacks. By dynamically partitioning IIoT into subnetworks based on localized attack features and reconstructing each subnetwork with tailored topologies, our framework significantly improves connectivity and communication efficiency. Evaluations on a real-world dataset (Tech-Routers-RF) characterizing IIoT topologies with 2113 nodes show that under diverse attack scenarios, connectivity and communication efficiency improve by more than 70% and 50%, respectively. Leveraging information entropy to quantify the trade-off between structural diversity and connection predictability, our work bridges adaptive network design with real-world attack dynamics, offering a scalable solution for securing large-scale IIoT deployments.

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Publication Details

Journal
Entropy
Published
2025-05-07
DOI
https://doi.org/10.3390/e27050503
Citations
2
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
2.97

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article

Resilient Topology Reconfiguration for Industrial Internet of Things: A Feature-Driven Approach Against Heterogeneous Attacks

Wenli Shang, Xianda Liu, Dong Li, Tianyu Wang et al.
2 citations
Entropy
Software-Defined Networks and 5G
2.97
article

Resilient Topology Reconfiguration for Industrial Internet of Things: A Feature-Driven Approach Against Heterogeneous Attacks

Wenli Shang, Xianda Liu, Dong Li, Tianyu Wang, Bowen Zhang
article en
2 citations

Abstract

This paper proposes a feature-driven topology reconfiguration framework to enhance the resilience of Industrial Internet of Things (IIoT) systems against heterogeneous attacks. By dynamically partitioning IIoT into subnetworks based on localized attack features and reconstructing each subnetwork with tailored topologies, our framework significantly improves connectivity and communication efficiency. Evaluations on a real-world dataset (Tech-Routers-RF) characterizing IIoT topologies with 2113 nodes show that under diverse attack scenarios, connectivity and communication efficiency improve by more than 70% and 50%, respectively. Leveraging information entropy to quantify the trade-off between structural diversity and connection predictability, our work bridges adaptive network design with real-world attack dynamics, offering a scalable solution for securing large-scale IIoT deployments.

EntropyVol. 27(5)
Shenyang Institute of Automation (CN), Chinese Academy of Sciences (CN), Guangzhou University (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Industry, innovation and infrastructure
Openalex Percentile: Top 9%
Software-Defined Networks and 5G
2.97
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