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An access control conflict resolution model based on inductive learning of answer set program

The existing methods for automatically implementing access control policies are mainly traditional logic programming, that is, monotonic logic. There are some shortcomings in monotonic logic, the most important of which is that its design does not invalidate the initial belief in later observation. This limitation makes traditional logic methods not suitable for modeling and analyzing context-aware access control policies. When we need to enforce exceptions at runtime, inconsistencies and conflicts occur that cannot invalidate the initial strategy. Therefore, we propose a non-monotonic logic-based adaptive access control policy modeling and analysis inference scheme that can expressly indicate unavailable context data and incomplete access control policies. In this article, a formal method, Question Set Programming (ASP), is proposed to elegantly represent the unavailability of context data. In the following, we will automatically learn conflict resolution methods through cautious induction learning methods to make the modeling and analysis of adaptive access control policies more comprehensive and intelligent.