Over-Fitting and Error Detection for Online Role Mining

Abstract

Recent research has attempted to use role-based approaches to recommend mobile services to other members among the same group in a context dependent manner. However, the traditional role mining approaches originated from the domain of security control tend to be rigid and may not be able to capture human behaviors adequately. In particular, during the course of role mining process, these approaches easily result in over-fitting, i.e., too many roles with slightly different service consumption patterns are found. As a result, they fail to reveal the true common preferences within the user community. This paper proposes an online role mining algorithm with a residual term and an error term, that automatically group users according to their interests and habits without losing sight of their individual preferences and random errors. Moreover, to resolve the over-fitting problem, the authors relax the role definition in role mining mechanism by introducing quasi-roles based on the concept of quasi-bicliques. Most importantly, the new concept allows us to propose a monitoring framework to detect and correct over-fitting in online role mining such that recommendations can be made based on the latest and genuine common preferences. To the best of the authors’ knowledge, this is a new area in service recommendation that is yet to be fully explored.

Web Service Recommendations Based on Time-Aware Bayesian Networks

“…The knowledge can be discovered by a central server based on a consolidated Ψ t c gathered from a user community with user-role assignment matrix Θ t c and user-context-behavior relation matrix Ψ t c as outputs [17]. Role coverage vector RC t is a vector of area-of-roles in a role set as they appear in a Ψ t c matrix.…”

How to Discover High-Quality Roles? A Survey and Dependency Analysis of Quality Criteria in Role Mining

“…In contrast to most approaches (e.g. [3,53,60]), some techniques allow to deviate to a certain extent from the initial UP A-matrix based on a given threshold (the so called δ − RM P [51]) [5,35]. Completeness therefore measures the quality of a RBAC state by measuring the degree to which a resulting role set represents the initial access control situation.…”

“…In [29], for instance, the permission similarity is measured using the Euclidean Distance. Furthermore, the Jaccard Similarity is a popular metric used in a variety of approaches [5,40,57].…”

“…They can, for instance, be used to create a role set based on the location attribute of all user accounts. Distance measures are applied to identify redundant roles [5].…”

Analyzing Quality Criteria in Role-based Identity and Access Management

“…In (Jafari et al, 2009), for instance, the permission similarity is measured using the Euclidean Distance. Furthermore, the Jaccard Similarity is a popular metric used in a variety of approaches (Mandala et al, 2012;Xu and Stoller, 2013a;Chu et al, 2012).…”