Towards Automated Learning of Access Control Policies Enforced by Web Applications

Padmavathi Iyer, Amir Masoumzadeh

Abstract: Obtaining an accurate specification of the access control policy enforced by an application is essential in ensuring that it meets our security/privacy expectations. This is especially important as many of real-world applications handle a large amount and variety of data objects that may have different applicable policies. We investigate the problem of automated learning of access control policies from web applications. The existing research on mining access control policies has mainly focused on developing algorithms for inferring correct and concise policies from low-level authorization information. However, little has been done in terms of systematically gathering the low-level authorization data and applications' data models that are prerequisite to such a mining process. In this paper, we propose a novel black-box approach to inferring those prerequisites and discuss our initial observations on employing such a framework in learning policies from real-world web applications.