Open AccessCritical nodes identification for vulnerability analysis of power communication networks
Author(s) -
Fan Bing,
Zheng ChenXi,
Tang LiangRui,
Wu RunZe
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0179
Subject(s) - vulnerability (computing) , computer science , node (physics) , vulnerability assessment , computer network , complex network , distributed computing , identification (biology) , network topology , topology (electrical circuits) , grid , cascading failure , physical layer , power (physics) , computer security , electric power system , telecommunications , mathematics , engineering , psychological resilience , psychotherapist , structural engineering , world wide web , biology , psychology , geometry , quantum mechanics , wireless , botany , physics , combinatorics
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.