The Intrusion Detection Model of Medical Information Diversity Database under Cloud Computing

Abstract

In current large databases, there is a widespread defect of poor immunity. It is mainly due to the huge amount of data which makes the traditional algorithm falling into a defect of low efficiency of local search, causing inconspicuous effect to detect potential risks and other defects. To this end, we propose an intrusion detection model of medical information diversity database under cloud computing. By using the similar risk attributes of data, an intrusion test model under cloud computing environment is constructed. The use of intrusion tolerance theory can reduce the impact of failure report phenomenon for the stability of whole system. This model can effectively resist denial of service attacks, but also through the mutual cooperation between the servers to prevent the single failure node attacking to the system, ensure the safety and smooth of the cloud computing services at the greatest degree, in order to enhance the user experience. Experiments show that the algorithm improves the accuracy of the intrusion detection of medical information diversity database, and achieves good results.