RMPlib: A Library of Benchmarks for the Role Mining Problem

Abstract

Role Based Access Control is a widely spread concept in cyber security. Thus, the (NP-complete) Role Mining Problem (RMP), which consists of finding an optimal set of roles and a corresponding assignment of those roles to users, is of great scientific interest. Over the last years, different algorithms have been developed to search for good solutions to the RMP. However, conclusive benchmarks for thorough comparison of the developed methods are rarely known. This paper introduces to RMPlib, a library for the Role Mining Problem, containing a set of new industry-oriented benchmark instances partly taken from real-world use cases, partly created synthetically. Access to RMPlib is provided through a platform where researchers can actively contribute new benchmark instances and best solutions, such that the library adapts to the changing requirements in science. The current version of RMPlib can be found at https://github.com/RMPlib/RMPlib.

Attribute-Based Access Control Policy Generation Approach from Access Logs Based on the CatBoost

“…As this approach considers all predicates in the ACPs sentence, it leads to some false positives, which makes their approach's precision relatively low (precision of 75 %). Anderer et al [20] created a library of role mining benchmark instances, which includes some new, synthetically generated benchmark instances of different sizes for evaluating and comparing role mining algorithms. The benchmark instances leave more space between the number of roles derived from the two common decompositions of the role mining problem (RMP) and the actual minimum number of roles, thus making them better, multifaceted, and able to thoroughly evaluate the role mining algorithm.…”