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A Data Classification Method for Inconsistency and Incompleteness Detection in Access Control Policy Sets

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Abstract

Access control policies may contain anomalies such as incompleteness and inconsistency, which can result in security vulnerabilities. Detecting such anomalies in large sets of complex policies automatically is a difficult and challenging problem. In this paper, we propose a novel method for detecting inconsistency and incompleteness in access control policies with the help of data classification tools well known in data mining. Our proposed method consists of three phases: firstly, we perform parsing on the policy data set; this includes ordering of attributes and normalization of Boolean expressions. Secondly, we generate decision trees with the help of our proposed algorithm, which is a modification of the well-known C4.5 algorithm. Thirdly, we execute our proposed anomaly detection algorithm on the resulting decision trees. The results of the anomaly detection algorithm are presented to the policy administrator who will take remediation measures. In contrast to other known policy validation methods, our method provides means for handling incompleteness, continuous values and complex Boolean expressions. In order to demonstrate the efficiency of our method in discovering inconsistencies, incompleteness and redundancies in access control policies, we also provide a proof-of-concept implementation.

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Notes

  1. For this purpose, we have used the Sipina data mining software package developed by Ricco Rakotomalala in the ERIC Research laboratory [30].

  2. XAMCL, a standard policy specification language, was defined for this purpose. However, for ease of understanding and simplicity, we have specified rules in simple XML language.

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Acknowledgments

The work reported in this article was partially supported by the Natural Sciences and Engineering Research Council of Canada, PROMPT Quebec, and CA Technologies. We would like to thank Serge Mankovski of CA Technologies for having helped our effort. The authors would also like to thank all members of the Computer Security Research Lab (UQO,Canada), and Bernard Stepien for providing useful comments and suggestions.

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Authors and Affiliations

  1. Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia

    Riaz Ahmed Shaikh

  2. Computer Science and Engineering Department, Université du Québec en Outaouais, Gatineau, QC, Canada

    Kamel Adi & Luigi Logrippo

Authors
  1. Riaz Ahmed Shaikh
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  2. Kamel Adi
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  3. Luigi Logrippo
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Corresponding author

Correspondence to Riaz Ahmed Shaikh.

Appendix

Appendix

Decision trees for the rule set 1, 2, 3 and 4 are given in Figs. 10, 11, 12, and 13, respectively.

Fig. 10
figure 10

Decision tree for rule set 1

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Fig. 11
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Decision tree for rule set 2

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Fig. 12
figure 12

Decision tree for rule set 3

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Fig. 13
figure 13

Decision tree for rule set 4

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Shaikh, R.A., Adi, K. & Logrippo, L. A Data Classification Method for Inconsistency and Incompleteness Detection in Access Control Policy Sets. Int. J. Inf. Secur. 16, 91–113 (2017). https://doi.org/10.1007/s10207-016-0317-1

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