Istituto di Informatica e Telematica, Consiglio Nazionale delle Ricerche, 56124 Pisa, Italy.
Department of Information Engineering, University of Pisa, 56122 Pisa, Italy.
Sensors (Basel). 2020 May 23;20(10):2960. doi: 10.3390/s20102960.
The enforcement of fine-grained access control policies in constrained dynamic networks can become a challenging task. The inherit constraints present in those networks, which result from the limitations of the edge devices in terms of power, computational capacity and storage, require an effective and efficient access control mechanism to be in place to provide suitable monitoring and control of actions and regulate the access over the resources. In this article, we present RESPOnSE, a framework for the specification and enforcement of security policies within such environments, where the computational burden is transferred to high-tier nodes, while low-tier nodes apply risk-aware policy enforcement. RESPOnSE builds on a combination of two widely used access control models, Attribute-Based Access Control and Role-Based Access Control, exploiting the benefits each one provides. Moreover, the proposed mechanism is founded on a compensatory multicriteria decision-making algorithm, based on the calculation of the Euclidean distance between the run-time values of the attributes present in the security policy and their ideal values, as those are specified within the established policy rules.
在受限动态网络中执行细粒度访问控制策略可能成为一项具有挑战性的任务。这些网络中存在的固有约束是由于边缘设备在功率、计算能力和存储方面的限制造成的,这就需要一个有效和高效的访问控制机制来进行适当的监控和控制操作,并对资源进行访问控制。在本文中,我们提出了 RESPOnSE,这是一种在这种环境中指定和执行安全策略的框架,其中计算负担转移到高层节点,而底层节点则应用风险感知的策略执行。RESPOnSE 建立在两种广泛使用的访问控制模型的基础上,即基于属性的访问控制和基于角色的访问控制,利用了每种模型提供的优势。此外,所提出的机制是基于补偿多准则决策算法的,该算法基于安全策略中属性的运行时值与其理想值之间的欧几里得距离的计算,因为这些值是在已建立的策略规则中指定的。