- By:
- Kay, William W; Lu, Hao ; Devineni, Pravallika ; Tabassum, Anika; Chinthavali, Supriya ; Lee, Sangkeun M
- Page Number:
- 3685-3690
- Book Title:
- 2021 IEEE International Conference on Big Data (Big Data)
- Publication Date:
- November 9, 2023
- Publisher Location:
- IEEE, New Jersey, United States of America
- Conference Name:
- 2021 IEEE International Conference on Big Data
- Conference Location:
- Orlando, Florida, United States of America
- Conference Sponsor:
- IEEE
- View DOI Listing:
- https://doi.org/10.1109/BigData52589.2021.9671620
Abstract
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.
