A Network Immunization Strategy Based on Dynamic Preference Scan

There is a variety of self-organization phenomena emerging in complex networks. These phenomena bring enlightenment to the method of network vulnerability mining and the technology of network self-propelled immunization. A complete process of immunity resource deployment can be divided into four stages: information gathering, scanning, bug fixing and self-propulsion. The result of empirical analysis on the vulnerability distribution demonstrates that the distribution of vulnerable hosts obey the power law. It implies that blindfold scanning wastes many resources on invulnerable or inexistent hosts and a more effective immunization strategy should take advantage of this high non-uniformity of network vulnerability distribution. Good results can be achieved by static preference scan at the beginning of immunity resource spread. However, the effectiveness can not be persistent throughout the entire immunization process. On this basis, a novel network immunization self-propelled strategy is proposed, which is based on dynamic preference scan. This strategy can identify vulnerable hosts efficiently by a dynamic and adaptive preference scan method, and then fix and immunize these vulnerable hosts. This paper focuses on how to control this dynamic preference scan process. The analysis of modeling and computer simulation show that our strategy can restrain hazard spread efficiently and improve network security.