Mining Influential Communities for Virus Prevention in Cloud-Based Network

Abstract

In the study of cloud computing and its applications, security has been regarded as one of the greatest problems in the development of cloud computing. Therefore, the study and prevention of computer virus in cloud computing infrastructure becomes practically significant for cloud security. Based on the characteristics of the rapid spread of computer viruses, when a community which is composed by cloud nodes has a higher influence in the cloud-based network, it has a greater potential hazard if there are some viruses lurking in the community. Therefore, it's necessary to find the influential community for virus prevention in cloud infrastructure. To evaluate cross-community influence, this paper proposes a novel method that consists of two components: (1) the Mutual Evaluation Learning (MEL) model for evaluating the pairwise influence between two nodes (clients, servers) and (2) a cross-community influence ranking algorithm (CCIR) designed to quantify the influential strength of each cross-community based on MEL and PageRank. Empirical studies on numerous real networks demonstrate that the proposed method can adapt to different networking scenarios and can reasonably and effectively reflect the distribution of community influence.