Identification of Critical Infrastructure via PageRank

被引:2
|
作者
Kay, Bill [1 ]
Lu, Hao [1 ]
Devineni, Pravallika [1 ]
Tabassum, Anika [1 ]
Chintavali, Supriya [1 ]
Lee, Sangkeun Matt [1 ]
机构
[1] Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA
关键词
D O I
10.1109/BigData52589.2021.9671620
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Assessing critical infrastructure vulnerabilities is paramount to arranging efficient plans for their protection. Critical infrastructures are cyber-physical systems that can be represented as a network consisting of nodes and edges and highly interdependent in nature. Given the interdependent nature of critical infrastuctures, failure in one node may cause failure in many others resulting in a cascade of failures. In this paper, we propose a node criticality metric that uses Google's PageRank algorithm to identify nodes that are likely to fail (are vulnerable), nodes whose failure may cascade to many other sites in the network (are important), and nodes that are both vulnerable and important (are critical). We then present a series of experiments to understand how protecting certain critical nodes can help mitigate massive cascading failures. Simulating failures in a real-world network with and without critical node protections demonstrates the importance of identifying critical nodes in an infrastructure network.
引用
收藏
页码:3685 / 3690
页数:6
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