A malware propagation model considering software diversity and topology dynamics

In this paper, we propose a susceptible-exposed-infected-recovered-susceptible (SEIRS) epidemic model in scale-free networks (SFNs). The aim has been to model the impact of software diversity and a defense mechanism to reduce malware propagation in SFNs. The dynamics of network topology was considered during the propagation process. During the malware propagation, the network topology varies considering leaving and joining of nodes. Reproduction ratio as a measure of epidemics plays an important role in dynamic behavior of discrete-time SEIRS models. The number of diverse software packages installed on nodes is calculated and used as a parameter to prevent malware propagation. The accuracy of the model is investigated by comparing the numerical results of the analytical solution and simulation outputs.