基于相似度计算的ABAC静态策略更新算法

ABAC static strategy update algorithm based on similarity calculation

为了解决ABAC模型中大量策略所带来的系统复杂和冲突问题,提出了一种基于相似度计算的ABAC静态策略更新算法.该算法利用Jaccard相似度计算策略之间的相似度值,根据相似度值对策略集分组,在各组中再次根据相似度值处理冲突策略、删除冗余策略和合并相似度值高的策略.仿真实验结果表明了算法的准确性和有效性,在不影响最终决策的前提下可以较大程度地减少ABAC的策略数量和决策的时间,减轻系统负担.

A static policy update algorithm for ABAC based on similarity calculation was proposed to solve the system complexity and conflict problems caused by a large number of policies in the ABAC model.Jaccard similarity was used to calculate similarity values between policies,according to which the policy sets were grouped.In each group,the conflicting policies were processed,the redundant policies were deleted and the merge policies with high similarity values were dealt with again according to the similarity values.The simulation results showed the accuracy and effectiveness of the algorithm.Without affecting the final decision,the number of ABAC strategies and decision time were greatly reduced,with the system load reduced.