Evidence map›Paper›PMID 40422458›Full record

ArticleEntropy (Basel, Switzerland)2025

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

Tianyu Wang, Dong Li, Bowen Zhang, Xianda Liu, Wenli Shang

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Tianyu WangState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Dong LiState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.ORCID 0000-0002-4152-6370
Bowen ZhangKey Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.
Xianda LiuKey Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.
Wenli ShangSchool of Electronic and Communication Engineering, Guangzhou University, Guangzhou 510006, China.ORCID 0000-0001-6022-5381

Funding

National Industrial Internet Innovation and Development Program Project Development of Endogenous Security Industrial Switch EquipmentNational Key Research and Development Program of China 2024YFB4709000National Natural Science Foundation of China 62173101the Key Laboratory of Computing Power Network and Information Security, Ministry of Education 2024ZD018
6 · The paper itself

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.

Indexed as

adaptive securityheterogeneous threatsindustrial internet of things (IIoT)information entropynetwork resilienceresilient topology reconfiguration

Identifiers

PMID40422458
PMCPMC12110531

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.