Ontology-based access control model for security policy reasoning in cloud computing

There are many security issues in cloud computing service environments, including virtualization, distributed big-data processing, serviceability, traffic management, application security, access control, authentication, and cryptography, among others. In particular, data access using various resour...

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Bibliographic Details
Published in:The Journal of supercomputing 2014-03, Vol.67 (3), p.711-722
Main Authors: Choi, Chang, Choi, Junho, Kim, Pankoo
Format: Article
Language:English
Subjects:
Access control
Authentication
Cloud computing
Compilers
Computer information security
Computer Science
Dynamics
Interpreters
Policies
Processor Architectures
Programming Languages
Reasoning
Semantics
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Summary:There are many security issues in cloud computing service environments, including virtualization, distributed big-data processing, serviceability, traffic management, application security, access control, authentication, and cryptography, among others. In particular, data access using various resources requires an authentication and access control model for integrated management and control in cloud computing environments. Cloud computing services are differentiated according to security policies because of differences in the permitted access right between service providers and users. RBAC (Role-based access control) and C-RBAC (Context-aware RBAC) models do not suggest effective and practical solutions for managers and users based on dynamic access control methods, suggesting a need for a new model of dynamic access control that can address the limitations of cloud computing characteristics. This paper proposes Onto-ACM (ontology-based access control model), a semantic analysis model that can address the difference in the permitted access control between service providers and users. The proposed model is a model of intelligent context-aware access for proactively applying the access level of resource access based on ontology reasoning and semantic analysis method.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-013-0980-1