Research of malware propagation in complex networks based on 1-D cellular automata

In this paper, based on 1-D cellular automata, the probabilistic behaviors of malware propagation in complex networks are investigated. Neighborhood and state transition functions with integrated expression are established and two models of malware propagation are proposed to evaluate the probabilistic behavior of malware propagation in various networks. We run the proposed models on nearest-neighbor coupled network NC and Erdos-Renyi ER random graph network and Watts-StrogatzWS small world network and Barabasi-Albert BA power law network respectively. Analysis and simulations show that, the proposed models describe perfectly the dynamic behaviors of propagation in the above networks. Furthermore, the proposed models describe not only the average tendency of malware propagation but also the rare events such as saturation and extinction of malware, and overcome the limitation occurring in a deterministic model based on mean-field method that describes only the average tendency of malware propagation and neglects the probabilistic event. Meanwhile, the result of simulations shows that the heterogeneity of degree distribution and local spatial interaction are key factors affecting the malware propagation and immunization.