Estimating systematic risk in real-world networks

Abstract. Social, technical and business connections can all give rise to security risks. These risks can be substantial when individual compro-mises occur in combinations, and difficult to predict when some connec-tions are not easily observed. A significant and relevant challenge is to predict these risks using only locally-derivable information. We illustrate by example that this challenge can be met if some general topological features of the connection network are known. By simulat-ing an attack propagation on two large real-world networks, we identify structural regularities in the resulting loss distributions, from which we can relate various measures of a network’s risks to its topology. While de-riving these formulae requires knowing or approximating the connective structure of the network, applying them requires only locally-derivable information. On the theoretical side, we show that our risk-estimating methodology gives good approximations on randomly-generated scale-free networks with parameters approximating those in our study. Since many real-world networks are formed through preferential attachment mechanisms that yield similar scale-free topologies, we expect this methodology to have a wider range of applications to risk management whenever a large number of connections is involved