Defense against malware propagation in complex heterogeneous networks

Abstract

Devising appropriate defense strategies against malware propagation in complex networks with minimal budget is a challenging problem in research community. This paper studies and compares various immunization strategies such as random immunization, targeted immunization, acquaintance immunization and high-risk immunization to prevent the outbreak of malware. Also, three measures of node centrality (degree, closeness and betweenness) are taken into targeted immunization to slow down the malware propagation process. The malware propagation is modelled based on the susceptible–exposed–infected–recovered–susceptible with quarantine state (SEIRS-Q) epidemic model. Using numerical simulations, the model is verified with considering defense mechanisms in a synthetic (SFN) and a real (Facebook) network topology. The simulation results can help to better understand the effects of defense strategies against the malware propagation. The results show that the use of immunization and software diversity together are more effective than using each of them singly, in terms of reducing the density of infected node and halting malware propagation.