RESPOnSE
RESPOnSE enforces fine-grained access control policies in constrained dynamic networks by combining Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC) with a TOPSIS-inspired compensatory multicriteria decision-making algorithm to enable risk-aware policy enforcement while offloading heavy computation to high-tier nodes.
Key Features:
- Hybrid access control (ABAC + RBAC): Integrates Attribute-Based Access Control and Role-Based Access Control to combine attribute-driven decisions with role-based permission assignment.
- TOPSIS-inspired decision algorithm: Uses a compensatory multicriteria decision-making approach inspired by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS).
- Euclidean distance evaluation: Calculates the Euclidean distance between runtime attribute values and their ideal counterparts as specified in policy rules.
- Risk-aware enforcement on low-tier nodes: Enables low-tier nodes to perform risk-aware policy enforcement based on evaluated distances and runtime attributes.
- Computational offloading to high-tier nodes: Transfers computational burden to high-tier nodes to accommodate constrained power, computational capacity, and storage of edge devices.
Scientific Applications:
- Access control in constrained dynamic networks: Enforcing security policies in environments characterized by limited power, computational capacity, and storage.
- Risk-aware policy decision-making: Making access decisions that incorporate runtime attributes and assessed distances to policy-specified ideals.
- Hybrid policy management in complex networked environments: Combining ABAC and RBAC to support attribute-driven decisions alongside role-based permission structures.
Methodology:
Integrates ABAC and RBAC and applies a compensatory multicriteria decision-making algorithm inspired by TOPSIS that computes Euclidean distances between runtime attribute values and policy-specified ideal counterparts; computation is offloaded to high-tier nodes while low-tier nodes perform risk-aware enforcement.
Topics
Details
- Programming Languages:
- Python
- Added:
- 1/18/2021
- Last Updated:
- 2/6/2021
Operations
Publications
Michailidou C, Gkioulos V, Shalaginov A, Rizos A, Saracino A. RESPOnSE—A Framework for Enforcing Risk-Aware Security Policies in Constrained Dynamic Environments. Sensors. 2020;20(10):2960. doi:10.3390/s20102960. PMID:32456150. PMCID:PMC7285324.