Polisma - A Framework for Learning Attribute-Based Access Control Policies

Abstract

Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is to have a secure system which enables information sharing and information flow protection. Policy-based management system is a key mechanism for secure selective sharing of protected resources. However, policies in each party of such a collaborative environment cannot be static as they have to adapt to different contexts and situations. One advantage of collaborative applications is that each party in the collaboration can take advantage of knowledge of the other parties for learning or enhancing its own policies. We refer to this learning mechanism as policy transfer. The design of a policy transfer framework has challenges, including policy conflicts and privacy issues. Policy conflicts typically arise because of differences in the obligations of the parties, whereas privacy issues result because of data sharing constraints for sensitive data. Hence, the policy transfer framework should be able to tackle such challenges by considering minimal sharing of data and support policy adaptation to address conflict.In the paper we propose a framework that aims at addressing such challenges. We introduce a formal definition of the policy transfer problem for attribute-based policies. We then introduce the transfer methodology that consists of three sequential steps. Finally we report experimental results.

Design of the Secure Smart Home System Based on the Blockchain and Cloud Service

“…The basic framework of the attribute-based access control (ABAC) model [16] is shown in Figure 5. The basic steps of the authorization process of the access control policy are as follows:…”

An IoT Crossdomain Access Decision‐Making Method Based on Federated Learning

“…Karimi et al [17] propose a policy mining method based on unsupervised learning algorithm, which mines policies from the extracted policy rule pattern. Jabal et al [18] propose a framework for learning ABAC policies from examples and context information. The framework achieves good results in both real logs and synthetic logs.…”

PAMMELA: Policy Administration Methodology using Machine Learning

“…The authors have also proposed rule pruning and policy refinement techniques. [14] presents a framework known as Polisma for learning ABAC policies from access logs by using a combination of statistical, data mining and machine learning algorithms. In [16], the authors have designed a policy learning method that is adaptive in nature using a feedback loop and is applicable for home Internet of Things (IoT) environment.…”

“…They have modeled the problem of ABAC policy learning as a reinforcement learning problem. Very recently, Bertino et al have proposed an approach known as FLAP [15] for collaborative environments. FLAP enables one organization to learn policies from another organization and perform policy adaptation via a policy learning framework by using local log or local policies or local learning or hybrid learning.…”

“…FLAP enables one organization to learn policies from another organization and perform policy adaptation via a policy learning framework by using local log or local policies or local learning or hybrid learning. This work makes use of Polisma [14].…”