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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).
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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.
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.
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.
The first step is to set up a suitable data set for the analytics which consists of all user permission assignments (UPA) and a set of contextual data concerning permission activations aggregated per day (see Fig. 6).
These algorithms operate on different types of data, e.g. log files, roles or user permission assignments, and can be adjusted according to the available access control model and available data sources.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com