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Register a new userPML: An Interpreter-Based Access Control Policy Language for Web Services
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Added by
Yang Luo
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Access control is an important component for web services such as a cloud. Current clouds tend to design the access control mechanism together with the policy language on their own. It leads to two issues: (i) a cloud user has to learn different policy languages to use multiple clouds, and (ii) a cloud service provider has to customize an authorization mechanism based on its business requirement, which brings high development cost. In this work, a new access control policy language called PERM modeling language (PML) is proposed to express various access control models such as access control list (ACL), role-based access control (RBAC) and attribute-based access control (ABAC), etc. PMLs enforcement mechanism is designed in an interpreter-on-interpreter manner, which not only secures the authorization code with sandboxing, but also extends PML to all programming languages that support Lua. PML is already adopted by real-world projects such as Intels RMD, VMwares Dispatch, Oranges Gobis and so on, which proves PMLs usability. The performance evaluation on OpenStack, CloudStack and Amazon Web Services (AWS) shows PMLs enforcement overhead per request is under 5.9us.
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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.
Session management is a fundamental component of web applications: despite the apparent simplicity, correctly implementing web sessions is extremely tricky, as witnessed by the large number of existing attacks. This motivated the design of formal methods to rigorously reason about web session security which, however, are not supported at present by suitable automated verification techniques. In this paper we introduce the first security type system that enforces session security on a core model of web applications, focusing in particular on server-side code. We showcase the expressiveness of our type system by analyzing the session management logic of HotCRP, Moodle, and phpMyAdmin, unveiling novel security flaws that have been acknowledged by software developers.
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Atomizing various Web activities by replacing human to human interactions on the Internet has been made indispensable due to its enormous growth. However, bots also known as Web-bots which have a malicious intend and pretending to be humans pose a severe threat to various services on the Internet that implicitly assume a human interaction. Accordingly, Web service providers before allowing access to such services use various Human Interaction Proofs (HIPs) to authenticate that the user is a human and not a bot. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is a class of HIPs tests and are based on Artificial Intelligence. These tests are easier for humans to qualify and tough for bots to simulate. Several Web services use CAPTCHAs as a defensive mechanism against automated Web-bots. In this paper, we review the existing CAPTCHA schemes that have been proposed or are being used to protect various Web services. We classify them in groups and compare them with each other in terms of security and usability. We present general method used to generate and break text-based and image-based CAPTCHAs. Further, we discuss various security and usability issues in CAPTCHA design and provide guidelines for improving their robustness and usability.
Service-oriented architecture (SOA) system has been widely utilized at many present business areas. However, SOA system is loosely coupled with multiple services and lacks the relevant security protection mechanisms, thus it can easily be attacked by unauthorized access and information theft. The existed access control mechanism can only prevent unauthorized users from accessing the system, but they can not prevent those authorized users (insiders) from attacking the system. To address this problem, we propose a behavior-aware service access control mechanism using security policy monitoring for SOA system. In our mechanism, a monitor program can supervise consumers behaviors in run time. By means of trustful behavior model (TBM), if finding the consumers behavior is of misusing, the monitor will deny its request. If finding the consumers behavior is of malicious, the monitor will early terminate the consumers access authorizations in this session or add the consumer into the Blacklist, whereby the consumer will not access the system from then on. In order to evaluate the feasibility of proposed mechanism, we implement a prototype system. The final results illustrate that our mechanism can effectively monitor consumers behaviors and make effective responses when malicious behaviors really occur in run time. Moreover, as increasing the rules number in TBM continuously, our mechanism can still work well.
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