Study On Virus Spread Based On Cellular Automata And Weighted Network Model

With the development and popularization of network, varieties of computer viruses have quickly spread through the Internet. Its widespread formed a great threat to the whole computer network security. It will be an important subject of anti-virus research to defense and control viruses based on understanding their characteristics and laws. Considering the practical extreme complexity, wide covering scope of network and harms of virus spread, it had to use simulation technology when we research computer virus spread which based on the network platform. And here come two questions. One is how to simulate the practical network, while the other is how to simulate the law of virus spread on the network. What’s more, the research of virus spread is based on simulation of the complex network. Only when completing the network simulation, can the research on virus spread be made.In the past, people mainly studied the virus spreading behavior in the un-weighted network. However, most of the practical networks often show weighted property, so it is more meaningful to research the virus spreading behavior in weighted networks. This paper made profound research on virus spreading behavior in weighted networks based on the weighted network model and cellular automata method. The research work mainly included the following aspects:(1) In order to reflect the dynamic evolution behavior of more comprehensive and microcosmic network topology structure, this paper established a virus spreading model in weighted network. This model changed the original single connection selection, and advanced a new connection selection mechanism combining partial priority and randomness, which made the connection of nodes in two ways. It increased the influence on the network evolution behavior when the connection border of inner nodes died out. Starting from four partial affairs of nodes’increasing and deleting, borders’increasing and deleting, it not only can reflect the influence of weight value’s dynamic increasing on topology evolution, but also can reflect the influence of border weight value’s decreasing on network evolution, so that it can reflect the evolution process of practical network more factually. (2) Based on the virus spread weighted network model, in allusion to the phenomenon that the denser nodes are, larger ratio to be infected, it expanded the infection mechanism in un-weighted network, and put forward a micro infection mechanism based on neighbor infection weight.(3) In order to better reflect the dynamic evolution behavior of interaction among nodes when virus spread in partial microcosmic, it constructed a cellular automata model of virus spread, using cellular automata method on the basis of the former two research achievement. The cellular automata model has flexible structure, and can change controlling strategy in the evolution process, study the influence of each factor on virus spread, so that can effectively conquer the limitation that the differential equation constructed by average field method can only reflect the general trend of virus spread.(4) Considering the influence of network dynamics and node dynamics on virus spread, through analysis of virus spread mechanism and dynamics, this paper organically combined the weighted network model and cellular automata model of virus spread, and made micro simulation of virus spread process under time-varying dynamic network topology structure from nodes status evolution and network topology evolution. The experiment proved the applicability and correctness of weighted network mode advanced in this paper, and then simulated the virus spread. Through changing parameter c and d, it analyzed the influence of different network topology structures on virus spread. Through changing the infection ratio P and recovery ratioβ, it studied the influence of different spreading parameters on virus spread, and got the conclusion that the decreasing of p and increasing ofβcan effectively reduce the viruses’spreading distance and infection degree. Through adjusting the user’s reaction time ft, it showed that the shortening of ft could effectively check viruses’extensive overspread. Finally, according to the analyzing discussion and conclusion of experiment, this paper put forward the suggestion to prevent viruses.