Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation.

Saved in:

Bibliographic Details
Title:	Vulnerable Node Detection in Complex Networks Based on Dynamic Comparator and Trusted Chain Propagation.
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

Description
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]
ISSN:	18200214
DOI:	10.2298/CSIS260407047J