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We investigate the effects of risk perception in a SIS model for malware propagating in different types of networks such as regular, random and scale-free. We assume that the perception of the risk of being infected rely on the fraction of neighbors that are infected. The effects are mainly affected by two parameters denoted by J and ??, which models the linear response and nonlinear effects respectively...
We study the influence of motion on malicious code propagating in scale-free networks of moving agents which additionally perform long-distance jumps. A small percentage of jumps in the agent motion is sufficient to destroy the local correlations and to produce a large decrease in the propagation threshold, well explained in terms of mean-field theory. This effect is similar to the crossover found...
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