New model aims to improve network resilience

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New model aims to improve network resilience

A computer model developed at the US University of Missouri could be used to help identify network vulnerabilities and to model worst-case scenarios.

"Our model allows users to identify the best or worst possible scenarios of network change," said Tim Matisziw, assistant professor of geography and engineering at MU. "The difficulty in evaluating a networks' resilience is that there are an infinite number of possibilities, which makes it easy to miss important scenarios. Previous studies focused on the destruction of large hubs in a network, but we found that in many cases the loss of smaller facilities can be just as damaging. Our model can suggest ways to have the maximum impact on a network with the minimum effort."

The model can be improved after every incident or outage: breakdown data fed into the model helps to identify further weak-points where future system failures could occur.

The model also can determine if a plan is likely to create the strongest network possible.

Matisziw's model was documented in the publicly available journal PLoS ONE. Making such a powerful tool widely available won't be a danger, Matisziw said. To use his model, a network must be understood in detail. Since terrorists and other criminals don't have access to enough data about the networks, they won't be able to use the model to develop doomsday scenarios.

The paper ‘Robustness Elasticity in Complex Networks’ can be viewed online at http://dx.plos.org/10.1371/journal.pone.0039788

Abstract:
Network robustness refers to a network’s resilience to stress or damage. Given that most networks are inherently dynamic, with changing topology, loads, and operational states, their robustness is also likely subject to change. However, in most analyses of network structure, it is assumed that interaction among nodes has no effect on robustness. To investigate the hypothesis that network robustness is not sensitive or elastic to the level of interaction (or flow) among network nodes, this paper explores the impacts of network disruption, namely arc deletion, over a temporal sequence of observed nodal interactions for a large Internet backbone system. In particular, a mathematical programming approach is used to identify exact bounds on robustness to arc deletion for each epoch of nodal interaction. Elasticity of the identified bounds relative to the magnitude of arc deletion is assessed. Results indicate that system robustness can be highly elastic to spatial and temporal variations in nodal interactions within complex systems. Further, the presence of this elasticity provides evidence that a failure to account for nodal interaction can confound characterizations of complex networked systems.

•Date: 12th July 2012 • US/World •Type: Article • Topic: ICT continuity

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