Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation.
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| Title: | Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation. |
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| Authors: | Jing, Dongsheng1 jds_sz@js.sgcc.com.cn, Qi, Baozhen1 qibz@js.sgcc.com.cn, Yang, Yu1 yang_yu@js.sgcc.com.cn, Xue, Jingsong1 js.xue@js.sgcc.com.cn, Feng, Renjun1 fengrj@js.sgcc.com.cn |
| Source: | Computer Science & Information Systems. Sep2026, Vol. 23 Issue 4, p1239-1258. 20p. |
| Subjects: | Fault diagnosis, Computer network security, Computer system failures, Comparator circuits, Computational complexity |
| Abstract: | Accurately identifying vulnerable nodes in complex networks is a critical challenge for preventing cascading failures and ensuring system robustness. Existing methods generally suffer from limitations such as static metrics that ignore dynamic loads, high parameter dependency, and a lack of unified fusion mechanisms. To address these issues, we propose an approach that is based on dynamic comparator selection and trusted chain propagation, which we refer to as Optimal Parameter (OP). Specifically, the proposed OP approach adaptively adjusts strategies based on node degree: low-degree nodes enable a lightweight mode to reduce overhead, while high-degree nodes employ full sampling to enhance reliability. State chain propagation is achieved through trusted queues, reducing the global detection complexity to linear order. Furthermore, the method integrates temporal consistency checks, faulty comparator isolation, and cross-layer comparison mechanisms to effectively handle complex scenarios such as malicious camouflage and low connectivity. Experimental results demonstrate that OP achieves a higher true positive rate and a lower false positive rate across multiple real-world datasets. It shows significant advantages, particularly at high attack edge density, thereby verifying its synergistic performance in terms of high accuracy and efficiency in largescale networks. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 197881221 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Jing%2C+Dongsheng%22">Jing, Dongsheng</searchLink><relatesTo>1</relatesTo><i> jds_sz@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Qi%2C+Baozhen%22">Qi, Baozhen</searchLink><relatesTo>1</relatesTo><i> qibz@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Yang%2C+Yu%22">Yang, Yu</searchLink><relatesTo>1</relatesTo><i> yang_yu@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Xue%2C+Jingsong%22">Xue, Jingsong</searchLink><relatesTo>1</relatesTo><i> js.xue@js.sgcc.com.cn</i><br /><searchLink fieldCode="AR" term="%22Feng%2C+Renjun%22">Feng, Renjun</searchLink><relatesTo>1</relatesTo><i> fengrj@js.sgcc.com.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Sep2026, Vol. 23 Issue 4, p1239-1258. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Fault+diagnosis%22">Fault diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+security%22">Computer network security</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+system+failures%22">Computer system failures</searchLink><br /><searchLink fieldCode="DE" term="%22Comparator+circuits%22">Comparator circuits</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Accurately identifying vulnerable nodes in complex networks is a critical challenge for preventing cascading failures and ensuring system robustness. Existing methods generally suffer from limitations such as static metrics that ignore dynamic loads, high parameter dependency, and a lack of unified fusion mechanisms. To address these issues, we propose an approach that is based on dynamic comparator selection and trusted chain propagation, which we refer to as Optimal Parameter (OP). Specifically, the proposed OP approach adaptively adjusts strategies based on node degree: low-degree nodes enable a lightweight mode to reduce overhead, while high-degree nodes employ full sampling to enhance reliability. State chain propagation is achieved through trusted queues, reducing the global detection complexity to linear order. Furthermore, the method integrates temporal consistency checks, faulty comparator isolation, and cross-layer comparison mechanisms to effectively handle complex scenarios such as malicious camouflage and low connectivity. Experimental results demonstrate that OP achieves a higher true positive rate and a lower false positive rate across multiple real-world datasets. It shows significant advantages, particularly at high attack edge density, thereby verifying its synergistic performance in terms of high accuracy and efficiency in largescale networks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS260407047J Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1239 Subjects: – SubjectFull: Fault diagnosis Type: general – SubjectFull: Computer network security Type: general – SubjectFull: Computer system failures Type: general – SubjectFull: Comparator circuits Type: general – SubjectFull: Computational complexity Type: general Titles: – TitleFull: Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Jing, Dongsheng – PersonEntity: Name: NameFull: Qi, Baozhen – PersonEntity: Name: NameFull: Yang, Yu – PersonEntity: Name: NameFull: Xue, Jingsong – PersonEntity: Name: NameFull: Feng, Renjun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 4 Titles: – TitleFull: Computer Science & Information Systems Type: main |
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