Critical nodes identification for vulnerability analysis of power communication networks. Issue 4 (1st March 2020)

• Record Type: Journal Article
• Authors: Fan, Bing; Zheng, Chen‐Xi; Tang, Liang‐Rui; Wu, Run‐Ze
• Is Part Of: IET communications. Volume 14:Issue 4(2020)
• Page Start: 703
• Page End: 713
• Publication Date: 2020-03-01
• Subjects: telecommunication network topology -- telecommunication security -- security of data -- power grids -- telecommunication traffic; power communication networks -- electric power grid -- PCNs -- network vulnerability -- vulnerability indicators -- node importance -- multilayer critical nodes identification algorithm -- node critical degree; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0179
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : As the support networks of the electric power grid, power communication networks (PCNs) become more complex and vulnerable due to the increasing scale of the electric power grid. Identifying and protecting the critical nodes in PCNs in advance is an effective way to reduce network vulnerability. Owing to the large differences in vulnerability indicators of different layers in the PCN, it is difficult to find the critical nodes, which have great impacts on all vulnerability indicators. Therefore, the goal of this study is to identify the critical nodes that have greater impacts on different layers, rather than nodes that have the greatest impact on a single layer. Therefore, the authors present a model to analyse the node in the PCNs from the physical topology, traffic distribution, and service importance distribution to calculate the node importance in the physical topology layer, the transport layer, and the service layer, respectively. Combined with a multi‐layer critical nodes identification algorithm (MCNIA) proposed, the node critical degree is obtained so that it can identify the critical nodes in the PCNs. The vulnerability analyses of PCNs under critical nodes attacking prove that MCNIA can identify critical nodes in the PCNs precisely.