On the robustness of network infrastructures to disasters and physical attacks

Abstract

Networks are vulnerable to natural disasters, such as earthquakes or floods, as well as to physical attacks, such as an Electromagnetic Pulse (EMP) attack. Such realworld events happen in specific geographical locations and disrupt specific parts of the network. Therefore, the geographical layout of the network determines the impact of such events on the network's connectivity. We focus on network analysis and design under a geographic failure model of (geographical) networks to such disasters. Initially, we aim to identify the most vulnerable parts of data networks to attack. That is, the locations of a disaster that would have the maximum disruptive effect on a network in terms of capacity and connectivity. We consider graph models in which nodes and links are geographically located on a plane, and model the disaster event as a line segment or circular disk. We develop polynomial time algorithms for finding the worst possible cut in this setting. Then, we obtain numerical results for a specific backbone network, thereby demonstrating the applicability of our algorithms to real-world networks. We also develop tools to calculate network metrics after a 'random' geographic disaster. The random location of the disaster allows us to model situations where the physical failures are not targeted attacks. In particular, we consider disasters that take the form of a 'random' circular disk or line in a plane. Using results from geometric probability, we are able to calculate some network performance metrics to such a disaster in polynomial time. In particular, we can evaluate average two-terminal reliability in polynomial time under these 'random' cuts. This is in contrast to the case of independent link failures for which there exists no known polynomial time algorithm to calculate this reliability metric. We present some numerical results to show the significance of geometry on the survivability of the network. This motivates the formulation of several network design problems in the context of randomly located disasters. We also study some min-cut and max-flow problems in a geographical setting. Specifically, we consider the problem of finding the minimum number of failures, modeled as circular disks, to disconnect a pair of nodes and the maximum number of failure disjoint paths between a pair of nodes. This model applies to the scenario where an adversary is attacking the network multiple times with intention to reduce its connectivity. We present a polynomial time algorithm to solve the geographic mincut problem and develop an ILP formulation, an exact algorithm, and a heuristic algorithm for the geographic max-flow problem. Finally, we study the reliability of power transmission networks under regional disasters. Initially, we quantify the effect of large-scale non-targeted disasters and their resulting cascade effects on power networks. We then model the dependence of data networks on the power systems and consider network reliability in this dependent network setting. Our novel approach provides a promising new direction for modeling and designing networks to lessen the effects of geographical disasters or attacks.