Detection of Conflicts Between Resource Authorization Rules in Extensible Access Control Markup Language Based on Dynamic Description Logic

Abstract

For resources in an open environment, the access control rules (ACRs), which are described by extensible access control markup language (XACML), might have conflicts between each other. To improve the rule management, the root causes of rule conflicts must be identified. This paper firstly formally models the resource attributes by dynamic description logic (DDL), and then investigates inference problems like attribute consistency and rule satisfiability by setting up concept, instance and action knowledge bases. Next, DDL-based rule conflict detection algorithms were designed to identify possible rule conflicts. Finally, the feasibility and decidability of the proposed algorithms were verified through experiments on expanded Continue dataset. The research results provide new insights to the detection of conflicts between resource authorization rules (RARs).

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

“…For the detection of data conflict in multisource data fusion, the abnormal points in conflict are regarded as outliers, and the point outlier detection technology is used to detect and process the conflict [19][20][21][22]. In the traditional data mining work, outlier detection is carried out by using statistics, clustering, classification, proximity, and other methods [23][24][25][26][27][28][29].…”

A Formal Validation Approach for XACML 3.0 Access Control Policy

“…Bryans explores the use of process algebra for formalizing and analyzing XACML language, presenting the core concepts of XACML using Communicating Sequential Processes (CSP) [26]. Other woks propose different formalization approaches for capturing the semantics of XACML constructs [27][28][29] and specifying an access control meta-model to derive access control policies or rule-based policies [30][31][32].…”