Optimal Mining on Type Control policy for Cloud Environment

In order to strengthen the cloud system’s ability to type control data resources, The information flow control model can more effectively protect the confidentiality and integrity of users' data in the cloud environment, and can prevent system vulnerabilities or attacks by illegal users outside the cloud system. However, in the process of implementing the type control system, too much manpower is often required to formulate rules to divide user security type labels. Unreasonable security type label division will directly affect system security and usability. In order to solve the problem of information flow control security type label distribution in the process of authorization system migration in a scientific way. This chapter proposes a bottom-up information flow access control security type label optimization mining method. The category domain label mining algorithm based on Louvain community discovery algorithm and the secret level mining algorithm based on genetic algorithm are used to obtain the optimal approximate solution to Information flow control security type label optimization mining problem (IFCSLMP). The results show that the proposed scheme can effectively dig out the effective information flow control security type label from the access control matrix.