Citation

Vivek S, Conner H. Safety Sci. 2022; 147: e105575.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2021.105575

PMID

unavailable

Abstract

The rise of connected vehicles and intelligent transportation lead to the emergence of novel complex risks. Of particular concern is the potential for large-scale attacks to disrupt road transportation, which is the lifeline of cities. This concern has only been growing with the increase in cybersecurity incidents and disinformation attacks in related infrastructures. In this study, we develop a framework to quantify, detect, and mitigate cascading consequences of attacks on road transportation networks. Application of our framework to the road network of Boston reveals that targeted attacks on a small fraction of nodes leads to disproportionately larger disruptions of routes. We develop an unsupervised machine learning algorithm based on network percolation theory and density based clustering (P-DBSCAN) to quantify risk for urban networks based on real-time traffic data. Our study illustrates a holistic approach to build resilience in existing road networks to attacks. Finally, we discuss the applicability of our framework in other smart city infrastructures.

Language: en

Keywords

Complex networks; Critical Infrastructure; Cyber-attacks; Smart city safety; Unsupervised machine learning; Urban road networks