Mitigation of malicious attacks on networks

Abstract

Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How and at which cost can one restructure the network such that it will become more robust against a malicious attack? We introduce a new measure for robustness and use it to devise a method to mitigate economically and efficiently this risk. We demonstrate its efficiency on the European electricity system and on the Internet as well as on complex networks models. We show that with small changes in the network structure (low cost) the robustness of diverse networks can be improved dramatically whereas their functionality remains unchanged. Our results are useful not only for improving significantly with low cost the robustness of existing infrastructures but also for designing economically robust network systems.percolation | power grid T he vulnerability of modern infrastructures stems from their network structure having very high degree of interconnectedness that makes the system resilient against random attacks but extremely vulnerable to targeted raids (1-17). We developed an efficient mitigation method and discovered that with relatively minor modifications in the topology of a given network and without increasing the overall length of connections, it is possible to mitigate considerably the danger of malicious attacks. Our efficient mitigation method against malicious attacks is based on developing and introducing a unique measure for robustness. We show that the common measure for robustness of networks in terms of the critical fraction of attacks at which the system completely collapses, the percolation threshold, may not be useful in many realistic cases. This measure, for example, ignores situations in which the network suffers a significant damage, but still keeps its integrity. Besides the percolation threshold, there are other robustness measures based, for example, on the shortest path (18)(19)(20) or on the graph spectrum (21). They are, however, less frequently used for being too complex or less intuitive. In contrast, our unique robustness measure, which considers the size of the largest component during all possible malicious attacks, is as simple as possible and only as complex as necessary. Due to the ample range of our definition of robustness, we can assure that our process of reconstructing networks maintains the infrastructure as operative as possible, even before collapsing. ModelModeling Attack on Infrastructures. We begin by demonstrating the efficiency of our unique approach to improve the performance of two of the most fragile, but critical infrastructures, namely, the power supply system in Europe (22) as well as the global Internet at the level of service providers, the so-called point of presence (PoP) (23). The breakdown of any of these networks would constitute a major disaster due to the strong dependency of modern society on electrical power and Internet. ...

Network robustness of multiplex networks with interlayer degree correlations

“…The study on the network robustness aims not only for theoretical interests [26][27][28][29][30] but also for practical applications to design more resilient structures against random breakdowns or intentional attacks [31][32][33]. Backup pathway between a pair of nodes is a meaningful concept of the network robustness, captured by the connection between a pair through at least two paths, termed biconnectivity [34,35].…”

Error and attack vulnerability of temporal networks

“…Network robustness has been intensively studied in the past years [9,10]. Initially, such studies investigated connectivity after failures [11][12][13], using common theoretical [14] and empirical methodologies.…”

Breakdown of interdependent directed networks

“…The robustness of a network (7,8) is its ability to continue functioning after experiencing targeted attacks or random failures. These can be characterized by using percolation theory to analyze the critical thresholds (8,9) or be defined using the integrated size of the largest connected component during the attack period (7).…”