Risk Assessment of Power Communication Network Based on Node Importance

Fan, Bing; Zheng, Chenxi; Tang, Liangrui

doi:10.1109/imcec46724.2019.8984120

Cited by 6 publications

(11 citation statements)

References 2 publications

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“…In [18,22], they divide the services running in PCN into five categories, and use the feature index evaluation method to obtain the importance of services. In this study, we set five categories of services.…”

Section: Simulation Parametersmentioning

confidence: 99%

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Critical nodes identification for vulnerability analysis of power communication networks

et al. 2020

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Section: Simulation Parametersmentioning

confidence: 99%

“…According to [22], the service distribution in the network is shown in Table 1. According to the importance of nodes in Sect (2), the importance of nodes in three layers can be obtained, as shown in Table 2.…”

Section: Simulation In the Small Pcnmentioning

confidence: 99%

Critical nodes identification for vulnerability analysis of power communication networks

et al. 2020

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“…To solve the above problem, some works like [3][4][5][6][7] exploit importance recognition methods to reveal the influences of different nodes on bandwidth estimation. However, they mainly identify important nodes based on the physical topology of the network, such as node centrality, K-shell, structure hole, PageRank.…”

Section: Introductionmentioning

confidence: 99%

Fine-Grained Bandwidth Estimation for Smart Grid Communication Network

Luo¹,

Liao²,

Zhang³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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“…Finally, the importance of each node was calculated by multi-attribute decision-making method, and the key nodes were determined according to the importance. The network was further subdivided in reference [21], and then the three layers of the network were comprehensively considered. From the perspective of network edge set, the edge cross layer importance (ECI) was defined, and the whole network information entropy edge cross layer information entropy (ECE) was given as the vulnerability evaluation index of power communication network.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in our paper, considering more factors that affect nodes in the network, we propose a new 4-layer network model architecture. In terms of parameters, compared with [20][21][22], we add the voltage level and load capacity factors of the node to characterize the importance of the node physical layer. In the application layer, the small flow service related to power production is of great importance, so the existing algorithms only consider the flow is not comprehensive.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Cross-Layer Network Vulnerability of Power Communication Network Based on Multi-Dimensional and Multi-Layer Node Importance Analysis

et al. 2022

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Power communication network (PCN) is the cornerstone of the stable operation of smart grid. The evaluation of the essential vulnerability of the PCN based on the node importance is advantageous to the protection of critical nodes and the improvement of the reliability and controllability of the system. So, firstly, this paper establishes a multi-index evaluation system for the analysis of important nodes. Based on the site operation level, site load capacity, network topology, service flow, service quality requirements and security operation requirements, the network characteristics of nodes are divided into four layers to describe. Meanwhile, the characteristics of node topology are subdivided into three dimensions to analyze. Then, fully considering the coupling between power network and communication network, a multi-dimensional and multi-layer node importance (MMNI) model based on entropy weight method is proposed. Combined the long-term and short-term vulnerability loss, the node cross-layer vulnerability loss (CVL) function and network cross-layer loss entropy (CVE) model are proposed. Finally, based on the actual power communication network, under different attacks and fault strategies, the changes of maximum network connectivity and network cross layer loss entropy are compared. It shows the effectiveness and accuracy of the MMNI model for the analysis and evaluation of important nodes and it provides a theoretical basis for key weak point identification and the network optimization.

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Risk Assessment of Power Communication Network Based on Node Importance

Cited by 6 publications

References 2 publications

Critical nodes identification for vulnerability analysis of power communication networks

Critical nodes identification for vulnerability analysis of power communication networks

Fine-Grained Bandwidth Estimation for Smart Grid Communication Network

Evaluation of Cross-Layer Network Vulnerability of Power Communication Network Based on Multi-Dimensional and Multi-Layer Node Importance Analysis

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