A Realistic Model for Failure Propagation in Interdependent Cyber-Physical Systems
Abstract
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.
Recommended Citation
A. Sturaro et al., "A Realistic Model for Failure Propagation in Interdependent Cyber-Physical Systems," IEEE Transactions on Network Science and Engineering, vol. 7, no. 2, pp. 817 - 831, IEEE Computer Society, Apr 2020.
The definitive version is available at https://doi.org/10.1109/TNSE.2018.2872034
Department(s)
Computer Science
Research Center/Lab(s)
Center for High Performance Computing Research
Second Research Center/Lab
Intelligent Systems Center
Keywords and Phrases
Artificial intelligence; Cyber Physical System; Electric power transmission networks; Embedded systems; Outages; Robustness (control systems); Cascading failures; Communications networks; Failure propagation; Interdependent networks; Machine learning approaches; Machine learning methods; Network functionality; Smart grids; Learning systems; Machine learning
International Standard Serial Number (ISSN)
2327-4697
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 IEEE Computer Society, All rights reserved.
Publication Date
April-June 2020
Comments
This work is partially supported by the NSF grants CNS-1545037 and DGE1433659. Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission under the agreement No. PCIG11-GA-2012- 321980. This work is also partially supported by the TENACE PRIN Project 20103P34XC funded by the Italian MIUR, and by the Project "Tackling Mobile Malware with Innovative Machine Learning Techniques" funded by the University of Padua.