Permissions are highly sensitive in Internet-of-Things (IoT) applications, as IoT devices collect our personal data and control the safety of our environment. Rather than simply granting permissions, further constraints shall be imposed on permission usage so as to realize the Principle of Least Privilege. Since IoT devices are physically embedded, they are often accessed in a particular sequence based on their relative physical positions. Monitoring if such sequencing constraints are honoured when IoT devices are accessed provides a means to fence off malicious accesses. This paper proposes a history-based capability system, HCAP, for enforcing permission sequencing constraints in a distributed authorization environment. We formally establish the security guarantees of HCAP, and empirically evaluate its performance.
The State-Modifying Policy (SMP) language is an authorization logic that supports the specification of changes to the protection state as a result of authorization @cite_22 . SMP is designed particularly for HBAC policies. Its enforcement model, however, assumes a centralized resource guard (aka PEP), and is therefore incapable of supporting decentralized access control in the manner of HCAP.
@cite_22
1994582486 1577867985 1981058821 2562147820
Administering and maintaining access control systems is a challenging task, especially in environments with complex and changing authorization requirements. A number of authorization logics have been proposed that aim at simplifying access control by factoring the authorization policy out of the hard-coded resource guard. However, many policies require the authorization state to be updated after a granted access request, for example, to reflect the fact that a user has activated or deactivated a role. Current authorization languages cannot express such state modifications; these still have to be hard-coded into the resource guard. We present a logic for specifying policies where access requests can have effects on the authorization state. The logic is semantically defined by a mapping to Transaction Logic. Using this approach, updates to the state are factored out of the resource guard, thus enhancing maintainability and facilitating more expressive policies that take the history of access requests into account. We also present a sound and complete proof system for reasoning about sequences of access requests. This gives rise to a goal-oriented algorithm for finding minimal sequences that lead to a specified target authorization state. Many languages and algebras have been proposed in recent years for the specification of authorization policies. For some proposals, such as XACML, the main motivation is to address real-world requirements, typically by providing a complex policy language with somewhat informal evaluation methods; others try to provide a greater degree of formality --- particularly with respect to policy evaluation --- but support far fewer features. In short, there are very few proposals that combine a rich set of language features with a well-defined semantics, and even fewer that do this for authorization policies for attribute-based access control in open environments. In this paper, we decompose the problem of policy specification into two distinct sub-languages: the policy target language (PTL) for target specification, which determines when a policy should be evaluated; and the policy composition language (PCL) for building more complex policies from existing ones. We define syntax and semantics for two such languages and demonstrate that they can be both simple and expressive. PTaCL, the language obtained by combining the features of these two sub-languages, supports the specification of a wide range of policies. However, the power of PTaCL means that it is possible to define policies that could produce unexpected results. We provide an analysis of how PTL should be restricted and how policies written in PCL should be evaluated to minimize the likelihood of undesirable results. The Relationship-Based Access Control (ReBAC) model was recently proposed as a general-purpose access control model. It supports the natural expression of parameterized roles, the composition of policies, and the delegation of trust. Fong proposed a policy language that is based on Modal Logic for expressing and composing ReBAC policies. A natural question is whether such a language is representationally complete, that is, whether the language is capable of expressing all ReBAC policies that one is interested in expressing. In this work, we argue that the extensive use of what we call Relational Policies is what distinguishes ReBAC from traditional access control models. We show that Fong's policy language is representationally incomplete in that certain previously studied Relational Policies are not expressible in the language. We introduce two extensions to the policy language of Fong, and prove that the extended policy language is representationally complete with respect to a well-defined subclass of Relational Policies. We present a novel and scalable policy synthesis approach for robots. Rather than producing single-path plans for a static environment, we consider changing environments with uncontrollable agents, where the robot needs a policy to respond correctly over the infinite-horizon interaction with the environment. Our approach operates on task and motion domains, and combines actions over discrete states with continuous, collision-free paths. We synthesize a task and motion policy by iteratively generating a candidate policy and verifying its correctness. For efficient policy generation, we use grammars for potential policies to limit the search space and apply domain-specific heuristics to generalize verification failures, providing stricter constraints on policy candidates. For efficient policy verification, we construct compact, symbolic constraints for valid policies and employ a Satisfiability Modulo Theories (SMT) solver to check the validity of these constraints. Furthermore, the SMT solver enables quantitative specifications such as energy limits. The results show that our approach offers better scalability compared to a state-of-the-art policy synthesis tool in the tested benchmarks and demonstrate an order-of-magnitude speedup from our heuristics for the tested mobile manipulation domain.