a b s t r a c tContext: Access control is among the most important security mechanisms, and XACML is the de facto standard for specifying, storing and deploying access control policies. Since it is critical that enforced policies are correct, policy testing must be performed in an effective way to identify potential security flaws and bugs. In practice, exhaustive testing is impossible due to budget constraints. Therefore the tests need to be prioritized so that resources are focused on their most relevant subset. Objective: This paper tackles the issue of access control test prioritization. It proposes a new approach for access control test prioritization that relies on similarity. Method: The approach has been applied to several policies and the results have been compared to random prioritization (as a baseline). To assess the different prioritization criteria, we use mutation analysis and compute the mutation scores reached by each criterion. This helps assessing the rate of fault detection. Results: The empirical results indicate that our proposed approach is effective and its rate of fault detection is higher than that of random prioritization. Conclusion: We conclude that prioritization of access control test cases can be usefully based on similarity criteria.

As security requirements of software often change, developers may modify security policies such as access control policies (policies in short) according to evolving requirements. To increase confidence that the modification of policies is correct, developers conduct regression testing. However, rerunning all of existing system test cases could be costly and time-consuming. To address this issue, we develop a regression-test-selection approach, which selects every system test case that may reveal regression faults caused by policy changes. Our evaluation results show that our test-selection approach reduces a substantial number of system test cases efficiently.

Cloud computing promises scalable hosting by offering an elastic management of virtual machines which run on top of hardware data centers. This elastic management as a cornerstone of PaaS (Platform As A Service) has to deal with trade-offs between conflicting requirements such as cost and quality of service. Solving such trade-offs is a challenging problem. Indeed, most of PaaS providers consider only one optimization axis or ad-hoc multi-objective resolution techniques using domain specific heuristics.This paper aims at proposing a generic approach to build cloud optimization by combining modeling and search based paradigms. Our approach is two-fold: 1) To reason about a cloud environment, we use a Models@run.time approach to have an abstraction layer of a cloud configuration that supports monitoring capabilities and represents cloud intrinsic parameters like cost, load information, etc.. 2) We use a search-based algorithm to navigate through cloud candidate configuration solutions in order to solve the Cloud Multiobjective Optimization Problem (CMOP).We validate our approach based on a case study that we define with our cloud provider partner EBRC 1 as representative of a dynamic management problem of heterogeneous distributed cloud nodes. We implement a prototype of our PaaS supervision framework using Kevoree, a Models@run.time platform. The prototype 1

Abstract-Elasticity is a key feature for cloud infrastructures to continuously align allocated computational resources to evolving hosted software needs. This is often achieved by relaxing quality criteria, for instance security or privacy because quality criteria are often conflicting with performance. As an example, software replication could improve scalability and uptime while decreasing privacy by creating more potential leakage points. The conciliation of these conflicting objectives has to be achieved by exhibiting trade-offs. Multi-Objective Evolutionary Algorithms (MOEAs) have shown to be suitable candidates to find these trade-offs and have been even applied for cloud architecture optimizations. Still though, their runtime efficiency limits the widespread adoption of such algorithms in cloud engines, and thus the consideration of quality criteria in clouds. Indeed MOEAs produce many dead-born solutions because of the Darwinian inspired natural selection, which results in a resources wastage. To tackle MOEAs efficiency issues, we apply a process similar to modern biology. We choose specific artificial mutations by anticipating the optimization effect on the solutions instead of relying on the randomness of natural selection. This paper introduces the Sputnik algorithm, which leverages the past history of actions to enhance optimization processes such as cloud elasticity engines. We integrate Sputnik in a cloud elasticity engine, dealing with performance and quality criteria, and demonstrate significant performance improvement, meeting the runtime requirements of cloud optimization.

In order to facilitate managing authorization, access control architectures are designed to separate the business logic from an access control policy. To determine whether a user can access which resources, a request is formulated from a component, called a Policy Enforcement Point (PEP) located in application code. Given a request, a Policy Decision Point (PDP) evaluates the request against an access control policy and returns its access decision (i.e., permit or deny) to the PEP. With the growth of sensitive information for protection in an application, an access control policy consists of a larger number of rules, which often cause a performance bottleneck. To address this issue, we propose to refactor access control policies for performance improvement by splitting a policy (handled by a single PDP) into its corresponding multiple policies with a smaller number of rules (handled by multiple PDPs). We define seven attribute-set-based splitting criteria to facilitate splitting a policy. We have conducted an evaluation on three subjects of reallife Java systems, each of which interacts with access control policies. Our evaluation results show that (1) our approach preserves the initial architectural model in terms of interaction between the business logic and its corresponding rules in a policy, and (2) our approach enables to substantially reduce request evaluation time for most splitting criteria.