In this paper, we propose a discrete-time susceptible–exposed–infected–recovered–susceptible (SEIRS) epidemic model of malware propagation in scale-free networks (SFNs) with considering software diversity.

We study dynamical behavior of the SEIRS model, which is determined by a threshold (i.e., basic reproductive ratio).

Simulation results show that the proposed model, which considers software diversity, is more effective than other existing epidemic models.

We have compared different immunization mechanisms, and have shown that the targeted immunization is better than the random immunization for controlling malware spreading in SFNs.