Graph-Based Models for Multi-Tenant Security in Cloud Computing

Abstract

Multi-tenant cloud computing scenarios have a high level of security risks because tenants share the same hardware and network. Control of data privacy, access to resources and isolation of these resources pose a significant challenge. Therefore, the security challenges above can be addressed by employing the relatively new and exciting graph-based models that enable a more structured and reasoned representation of the relationships and interactions of tenants, resources, and services. In this paper, graph theory for managing multi-tenant cloud environments has been discussed to improve the security of the cloud environments through the sophisticated control of access, detection of anomalies, and risk assessments. These transformed cloud resources and tenant interaction can be modeled by graphs to build security models that are effective in monitoring risks, detecting pre-identified abnormalities and controlling for them where necessary. Further, the paper presents different graph-based approaches and methods including graph search, community identification and machine learning for anomaly detection for enhancing security of multi-tenanted cloud environments. These models prove to be useful for avoiding cross-tenancy data breaches, framework invasions, and battles for ambitious resources through realistic cases and concrete examples from VM deployment. The work also expresses the limitations of scaling, privacy issues, and compatibility with traditional security models as well as potential research areas considering the combination with AI and blockchain. In conclusion, graph-based models provide a rather sound approach to providing the specific multi-tenant security in the cloud, further developments of which will be crucial to the further improvement of cloud security.