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.

中文翻译：

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相互依赖的信息物理系统中故障传播的现实模型

现代网络物理系统正变得越来越相互依赖。这种相互依赖会产生新的漏洞，并使这些系统更容易出现故障。特别是，故障很容易在这些系统中传播，可能会导致级联效应，对其功能造成破坏性影响。在本文中，我们关注电网和通信网络之间的相互依赖，并提出了一种新的现实模型，称为 HINT（异构相互依赖网络），以研究级联故障的演变。我们的模型考虑了此类网络的异质性及其复杂的相互依赖性。我们使用 HINT 来训练基于新特征的机器学习方法，以预测级联故障的影响。此外，通过使用特征选择，我们确定了表征关键节点的最重要的特征。我们将 HINT 与之前提出的两个关于合成和真实网络拓扑的模型进行了比较。实验结果表明，现有模型过度简化了故障演变和网络功能要求。此外，机器学习方法可以准确预测所考虑场景中故障传播的影响。最后，我们表明，通过加强由所提出的特征识别的少数关键节点，我们可以大大提高网络的鲁棒性。机器学习方法可以准确预测所考虑场景中故障传播的影响。最后，我们表明，通过加强由所提出的特征识别的少数关键节点，我们可以大大提高网络的鲁棒性。机器学习方法可以准确预测所考虑场景中故障传播的影响。最后，我们表明，通过加强由所提出的特征识别的少数关键节点，我们可以大大提高网络的鲁棒性。