Virus Propagation Model Based On Asynchronous Cellular Automata In Adaptive Network

"The21st century will be the century of network ". With the development of network andinformation technology, people’s lives have been more convenient and diversification. At the sametime, how to restrain the spread of the virus in the network has become a hot issue among so manyscholars. The previous transmission researches of the virus in adaptive networks only consider theinteraction and feedback mechanisms between node dynamics and network dynamics, but ignorethe latency problems when the virus spread in the network. As we all know, there is end to enddelay between adjacent nodes in computer networks, and asynchronous cellular automata allows theevolution of network nodes which is not synchronized, which allows us to study the spread of thevirus in adaptive network with delay. The comprehensive considerations are also much closer to theactual network characteristics, therefore has fatal research significance.This research mainly includes the following aspects:(1) Brief introductions of the relevant background, theoretical basis of cellular automata andcomplex network are presented, the concepts of synchronous cellular automata grid, neighbors andstate set are described. Furthermore, the definition of Asynchronous Cellular Automata isintroduced companioned with the concepts of transfer function, read function and evolution set. Thedifference and relation between synchronous cellular automata and asynchronous cellular automataare analyzed. In addition, this paper describes several classical models of the network dynamics andthe transmission dynamics in the research of complex network.(2) Considering the adaptive node in the network dynamics, network dynamics of interactionand feedback mechanism, a discrete model of the spread of the virus in adaptive network withpropagation delay based on the asynchronous element cellular automata theory is proposed, thecorresponding cell evolution rule is set. The proposed model is named as SIS-De(susceptible-infected-susceptible-delay) virus propagation model.(3) For the established model, we create a network topology at the initial moment according toER random network, the WS small world network, and BA scale-free network rules featuresseparately. Simulation is carried out to analyze the spread of computer viruses in the proposedmodel from different perspectives, including spread evolution trend, the network degree distributionand transmission threshold. The results showed that the disconnected sides are reconnected because healthy nodes avoid the spread of the virus, and the node state evolved asynchronously due topropagation delay, the effect of these two points reduced the combined effect of the rate of spread ofthe virus and the outbreak scale.