An ACO-based Algorithm for Efficient XACML Policy Evaluation

DOI

10.2991/caai-17.2017.64How to use a DOI?

Keywords

XACML; efficient policy evaluation; ACO algorithm; euclidean distance method; ABAC

Abstract

With the explosive growth of the internet, XACML policies have grown rapidly in size and complexity, and the efficiency of ABAC decision-making is unable to meet people's increasing demands, so this paper serves to solve this problem by providing a model based on an ACO algorithm. The model first divides the XACML policy into different classifications by using an ACO algorithm, then searches for related policies by calculating the Euclidean Distance with the request attribute values and XACML policy center attribute values. This approach transforms the policy evaluation into a numerical calculation. To evaluate the efficiency and the effectiveness of these methods, the paper conducts two sets of tests. The first results shows that the classificationÿeffect of ACO algorithms is better than K-means; and the second results shows that the Euclidean Distance method is more efficient than the execution vectors used by Said Marouf, et al. to search for related policies.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yunpeng Zhang
AU  - Beibei Zhang
PY  - 2017/06
DA  - 2017/06
TI  - An ACO-based Algorithm for Efficient XACML Policy Evaluation
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 282
EP  - 288
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.64
DO  - 10.2991/caai-17.2017.64
ID  - Zhang2017/06
ER  -