A human-oriented virus propagation model in email networks

Our world is currently threatened by digital viruses, such as email viruses and mobile viruses. These viruses are mainly activated by users' operations. Therefore, it's important for us to understand the pattern of user's operational behaviors and estimate the effect of such behaviors on virus propagation. This paper first reveals the statistical characteristics of human behaviors, especially the email-checking intervals of the same user based on the Enron email dataset. After that, we analyze the effect of human operational behaviors and network topologies on virus propagation in a human-oriented virus propagation model. The empirical results from real dataset show that the waiting intervals of each user to check mailbox follow a long-tail distribution. Combining this finding, our experiments accurately describe the process of email-virus propagation. The results show that viruses can fast spread in a network if the email-checking intervals follow a long-tail distribution with a higher power-law exponent. Meanwhile, our results find that the infected nodes with the highest-degree may speed up the virus propagation through analyzing the effects of network structure on virus propagation.