sesa 18(16): e3

Research Article

Formal Approach to Detect and Resolve Anomalies while Clustering ABAC Policies

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  • @ARTICLE{10.4108/eai.13-7-2018.156003,
        author={Maryem Ait El Hadj and Ahmed Khoumsi and Yahya Benkaouz and Mohammed Erradi},
        title={Formal Approach to Detect and Resolve Anomalies while Clustering ABAC Policies},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={5},
        number={16},
        publisher={EAI},
        journal_a={SESA},
        year={2018},
        month={12},
        keywords={ABAC Policies, Clustering, Access Domain, Conflict, Redundancy, Detection and Resolution, Permissive Resolution, Restrictive Resolution},
        doi={10.4108/eai.13-7-2018.156003}
    }
    
  • Maryem Ait El Hadj
    Ahmed Khoumsi
    Yahya Benkaouz
    Mohammed Erradi
    Year: 2018
    Formal Approach to Detect and Resolve Anomalies while Clustering ABAC Policies
    SESA
    EAI
    DOI: 10.4108/eai.13-7-2018.156003
Maryem Ait El Hadj1,*, Ahmed Khoumsi2, Yahya Benkaouz3, Mohammed Erradi1
  • 1: Networking and Distributed Systems Research Group, ITM Team, ENSIAS, Mohammed V University in Rabat, Morocco
  • 2: Dept. Electrical & Comp. Eng., University of Sherbrooke, Canada
  • 3: Conception and Systems Laboratory, FSR, Mohammed V University in Rabat, Morocco
*Contact email: maryem_aitelhadj@um5.ac.ma

Abstract

In big data environments with big number of users and high volume of data, we need to manage the corresponding huge number of security policies. Using Attribute-Based Access Control (ABAC) model to ensure access control might become complex and hard to manage. Moreover, ABAC policies may be aggregated from multiple parties. Therefore, they may contain several anomalies such as conflicts and redundancies, resulting in safety and availability problems. Several policy analysis and design methods have been proposed. However, most of these methods do not preserve the original policy semantics. In this paper, we present an ABAC anomaly detection and resolution method based on the access domain concept, while preserving the policy semantics. To make the suggested method scalable for large policies, we decompose the policy into clusters of rules, then the method is applied to each cluster. We prove correctness of the method and evaluate its computational complexity. Experimental results are given and discussed.