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.

PMID: 32456150
PMCID: PMC7285324
Funding: - H2020 Marie Skłodowska-Curie Actions: 675320 - H2020 European Research Council: 700294