Modeling Malicious Code Spread in Scale-Free Networks of Moving Agents

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 in static small-world networks, and can be furthermore linked to the structural properties of the dynamical network of agent interactions.