Abstract
Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.
