An innovative defense strategy against targeted spreading in complex networks

Protecting target nodes in complex networks is a critical issue in network security research. In many real-world scenarios, the identities of certain target nodes remain unknown, and the impact of this incomplete information on appropriately selecting initial spreaders is not fully understood. This paper first examines how the observability rate of target nodes affects the effectiveness of targeted spreading. The findings indicate that even if most target nodes are unobservable, identifying the optimal spreader for targeted propagation is still feasible in many real-world networks. This indicates that solely relying on protecting target nodes through external observation avoidance is insufficient. To address this issue, we developed a novel camouflage defense strategy for target nodes in complex networks by integrating target centrality, the distribution of target node groups, and the network distance between disguised and hidden target nodes. This strategy effectively hinders attackers’ selection of the optimal initial spreader by adjusting the visibility of selected target nodes and their neighbors, without altering the network structure. Finally, we validate the effectiveness of the proposed model in three aspects: matching accuracy of the optimal initial spreader, implementation of SIR propagation dynamics, and comparative testing against other models. These results were obtained not only from three types of generic artificial networks but also from multiple real datasets. •We investigate the impact of incomplete info on selecting optimal initial spreaders.•We reveal the link between known target proportion and optimal spreader accuracy.•We develop a defense method integrating node centrality and network distance.•The clustering of target nodes further exacerbates their risk of targeted infection.•We verify the method’s effectiveness via propagation dynamics and model comparison.