A Realistic Model for Failure Propagation in Interdependent Cyber-Physical Systems

Agostino Sturaro, Simone Silvestri, Mauro Conti, Sajal K. Das

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


Modern cyber-physical systems are becoming increasingly interdependent. Such interdependencies create new vulnerabilities and make these systems more susceptible to failures. In particular, failures can easily spread across these systems, possibly causing cascade effects with a devastating impact on their functionalities. In this paper, we focus on the interdependence between the power grid and the communications network, and propose a novel realistic model, called HINT (Heterogeneous Interdependent NeTworks), to study the evolution of cascading failures. Our model takes into account the heterogeneity of such networks as well as their complex interdependencies. We use HINT to train machine learning methods based on novel features for predicting the effects of the cascading failures. Additionally, by using feature selection, we identify the most important features that characterize critical nodes. We compare HINT with two previously proposed models both on synthetic and real network topologies. Experimental results show that existing models oversimplify the failure evolution and network functionality requirements. In addition, the machine learning approaches accurately forecast the effects of the failure propagation in the considered scenarios. Finally, we show that by strengthening few critical nodes identified by the proposed features, we can greatly improve the network robustness.

Original languageEnglish
Article number8471213
Pages (from-to)817-831
Number of pages15
JournalIEEE Transactions on Network Science and Engineering
Issue number2
StatePublished - Apr 1 2020

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Smart grids
  • cascading failures.
  • interdependent networks
  • machine learning

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Computer Networks and Communications


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