Model-Based Analysis of Role-Based Access Control

Model-Driven Engineering (MDE) has been extensively studied. Many directions have been explored, sometimes with the dream of providing a fully integrated approach for designers, developers and other stakeholders to create, reason about and modify models representing software systems. Most, but not all, of the research in MDE has focused on general-purpose languages and models, such as Java and UML. Domain-specific and cross-cutting concerns, such as security, are increasingly essential parts of a software system, but are only treated as second-class citizens in the most popular modelling languages. Efforts have been made to give security, and in particular access control, a more prominent place in MDE, but most of these approaches require advanced knowledge in security, programming (often declarative), or both, making them difficult to use by less technically trained stakeholders. In this thesis, we propose an approach to modelling, analysing and automatically fixing role-based access control (RBAC) that does not require users to write code or queries themselves. To this end, we use two UML profiles and associated OCL constraints that provide the modelling and analysis features. We propose a taxonomy of OCL constraints and use it to define a partial order between categories of constraints, that we use to propose strategies to speed up the models’ evaluation time. Finally, by representing OCL constraints as constraints on a graph, we propose an automated approach for generating lists of model changes that can be applied to an incorrect model in order to fix it. All these features have been fully integrated into a UML modelling IDE, IBM Rational Software Architect

Model-Based Analysis of Role-Based Access Control

Model-Driven Engineering (MDE) has been extensively studied. Many directions have been explored, sometimes with the dream of providing a fully integrated approach for designers, developers and other stakeholders to create, reason about and modify models representing software systems. Most, but not all, of the research in MDE has focused on general-purpose languages and models, such as Java and UML. Domain-specific and cross-cutting concerns, such as security, are increasingly essential parts of a software system, but are only treated as second-class citizens in the most popular modelling languages. Efforts have been made to give security, and in particular access control, a more prominent place in MDE, but most of these approaches require advanced knowledge in security, programming (often declarative), or both, making them difficult to use by less technically trained stakeholders. In this thesis, we propose an approach to modelling, analysing and automatically fixing role-based access control (RBAC) that does not require users to write code or queries themselves. To this end, we use two UML profiles and associated OCL constraints that provide the modelling and analysis features. We propose a taxonomy of OCL constraints and use it to define a partial order between categories of constraints, that we use to propose strategies to speed up the models’ evaluation time. Finally, by representing OCL constraints as constraints on a graph, we propose an automated approach for generating lists of model changes that can be applied to an incorrect model in order to fix it. All these features have been fully integrated into a UML modelling IDE, IBM Rational Software Architect

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF