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As described above, we computed a criticality value for all user permission assignments.
Our applied algorithms measure the distribution of permission assignments among a defined set of users.
Therefore, we aim to qualify permission assignments based on activities carried out by users.
In our approach, each of these permission assignments would be detected by each run of the algorithms with all of the previously calibrated parameter sets.
For this effort, we used a set of parametrized anomaly detection algorithms for outlier detection algorithms [41] for the criticality determination of user permission assignments.
As mentioned above, the system currently manages over one million permission assignments (an average of about 147 assignments per user) with more than 30 million assignments possible.
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As a result, this permission assignment might probably be invalid and should be revoked.
Accordingly, we are able to classify the usage of every permission assignment on a daily level.
For the employee categorization, we followed the maximum principle according to the BSI Grundschutz [42] which states that the security level of an object should be as high as the highest of its associated resources (in our case the most critical user permission assignment): text{Criticality}_{text{employee}} = text{Max}~left text{criticality}_{text{UPA}}right).
To this aim, we propose a three steps methodology: first, we associate a weight to roles; second, we identify user-permission assignments that cannot belong to roles with a weight exceeding a given threshold; and third, we restrict the role-finding problem to user-permission assignments identified in the second step.
UP ⊆ U × P, is a user-permission assignment relation.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com