A Realistic Model for Failure Propagation in Interdependent Cyber-Physical Systems
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.
A. Sturaro et al., "A Realistic Model for Failure Propagation in Interdependent Cyber-Physical Systems," IEEE Transactions on Network Science and Engineering, IEEE Computer Society, Sep 2018.
The definitive version is available at https://doi.org/10.1109/TNSE.2018.2872034
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)
Article - Journal
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