Optimizing Machine Learning Based Access Control Administration Through Data Distillation.

Machine learning-based access control (MLBAC) shows promise in effectively determining access in complex scenarios where a trained ML model makes decisions. When access policies change, the underlying ML model must be updated to accommodate these changes. This process requires a portion of past training data, known as Replay Data, along with the new changes to retain existing knowledge and avoid challenges like catastrophic forgetting. Traditionally, Replay Data is selected randomly, constituting a large portion (approximately 25