An Automatic Attribute-Based Access Control Policy Extraction From Access Logs

With the rapid advances in computing and information technologies, traditional access control models have become inadequate terms of capturing fine-grained, expressive security requirements newly emerging applications. An attribute-based (ABAC) model provides a more flexible approach to addressing authorization needs complex dynamic systems. While organizations are interested employing newer models, migrating such pose as significant challenge. Many large-scale businesses need grant authorizations their user populations that potentially distributed across disparate heterogeneous environments. Each these environments may its own model. The manual development single policy framework for an entire organization is tedious, costly, error-prone. In this article, we present methodology automatically learning ABAC rules from logs system simplify process. proposed employs unsupervised learning-based algorithm detecting patterns extracting patterns. addition, two improvement algorithms, including rule pruning refinement algorithms generate higher quality mined policy. Finally, implement prototype demonstrate feasibility.