Continuous Learning of the Structure of Bayesian Networks: A Mapping Study

Bayesian networks can be built based on knowledge, data, or both. Independent of the source of information used to build the model, inaccuracies might occur or the application domain might change. Therefore, there is a need to continuously improve the model during its usage. As new data are collected, algorithms to continuously incorporate the updated knowledge can play an essential role in this process. In regard to the continuous learning of the Bayesian network’s structure, the current solutions are based on its structural refinement or adaptation. Recent researchers aim to reduce complexity and memory usage, allowing to solve complex and large-scale practical problems. This study aims to identify and evaluate solutions for the continuous learning of the Bayesian network’s structures, as well as to outline related future research directions. Our attention remains on the structures because the accurate parameters are completely useless if the structure is not representative

Secure Cloud Storage with Client-Side Encryption Using a Trusted Execution Environment

With the evolution of computer systems, the amount of sensitive data to be stored as well as the number of threats on these data grow up, making the data confidentiality increasingly important to computer users. Currently, with devices always connected to the Internet, the use of cloud data storage services has become practical and common, allowing quick access to such data wherever the user is. Such practicality brings with it a concern, precisely the confidentiality of the data which is delivered to third parties for storage. In the home environment, disk encryption tools have gained special attention from users, being used on personal computers and also having native options in some smartphone operating systems. The present work uses the data sealing, feature provided by the Intel Software Guard Extensions (Intel SGX) technology, for file encryption. A virtual file system is created in which applications can store their data, keeping the security guarantees provided by the Intel SGX technology, before send the data to a storage provider. This way, even if the storage provider is compromised, the data are safe. To validate the proposal, the Cryptomator software, which is a free client-side encryption tool for cloud files, was integrated with an Intel SGX application (enclave) for data sealing. The results demonstrate that the solution is feasible, in terms of performance and security, and can be expanded and refined for practical use and integration with cloud synchronization services

Issues in the Probability Elicitation Process of Expert-Based Bayesian Networks

A major challenge in constructing a Bayesian network (BN) is defining the node probability tables (NPT), which can be learned from data or elicited from domain experts. In practice, it is common not to have enough data for learning, and elicitation from experts is the only option. However, the complexity of defining NPT grows exponentially, making their elicitation process costly and error-prone. In this research, we conducted an exploratory study through a literature review that identified the main issues related to the task of probability elicitation and solutions to construct large-scale NPT while reducing the exposure to these issues. In this chapter, we present in detail three semiautomatic methods that reduce the burden for experts. We discuss the benefits and drawbacks of these methods, and present directions on how to improve them

Contribution to the implementation of a distributed system in real time for process control.

Neste trabalho propõe-se uma estrutura para um Sistema Distribuído Verticalmente Hierárquico para Controle de Processos. Introduz-se uma extensão ao Diagrama de Fluxo de Dados, como aplicado em Sistemas de Processamento de Dados, para a análise e representação de Sistemas de Automação e Controle, bem como técnicas de decomposição deste diagrama com objetivo de implementar-se o software do sistema de forma otimizada. Apresenta-se ainda a aplicação desta ferramenta, o Diagrama de Fluxo de Dados e Controle, para a análise e representação do Sistema Distribuído Verticalmente Hierárquico, bem como a contribuição a implementação do sistema proposto.A structure of a Vertically Hierarchical Distributed Process Control System is proposed. An extension of the Data Flow Diagram as applied to data processing system is proposed for analysis and representation of Automation and Control Systems. Decomposition techniques to be applied to this diagram for optimized software implementation is also presented. The proposed Data and Control Flow Diagram is applied for the analysis and representation of the proposed Vertically Hierarchical Distributed Process Control System. The contribution for the implememtation of the Vertically Hierarchical Distributed Process Control System is also presented

Concentration area: information processing.

Quando especificando, concebendo e analisando sistemas complexos, e necessário adotar uma methodologia composicional ou modular. Esta metodologia deve permitir ao projetista a habilidade de verificar propriedades locais de módulos ou componentes individuais de um sistema, e também, permitir a verificação do comportamento correto entre componentes interagindo. A aplicação de redes de Petri para a modelagem e verificação de sistemas, ao nível de especificação e concepção, e bem conhecida. A despeito de poderosos mecanismos de estruturação disponíveis na teoria de redes de Petri, para a construção do modelo de um sistema complexo, o projetista poderá ainda se defrontar com o problema da explosão de estados, quando analisando e verificando grandes sistemas. Neste trabalho introduzimos uma metodologia de analise composicional para um tipo de redes de Petri de alto-nível denominada G-Nets. Ainda mais, tendo em mente o objetivo de aplicar esta metodologia para sistemas atuais, nos abordamos a introdução sistemática de propriedades de tolerância a falhas na concepção de um componente. A aplicação e exemplificação da metodologia introduzida e na concepção de sistemas de software.When specifying, designing and analyzing complex systems, it is necessary to adopt a modular or compositional methodology. This methodology shall allow the designer the ability to verify local properties of individual modules or components in the system, and also shall allow the verification of the correct behavior of interacting components. The application of Petri nets for the modeling and verification of systems, at specification and design levels are well know. Despite of powerful structuring mechanisms available in the Petri nets theory for the construction of the model of complex systems, the designer is still likely to face the problem of state explosion, when analyzing and verifying large systems. In this work we introduce a compositional analysis methodology for a kind of high level Petri nets called G-Nets. Moreover, having in mind the objective of applying this methodology for actual systems, we also address the systematic introduction of fault-tolerant properties in the design of a component. The application and exemplification of the introduced analysis methodology is on the design of software systems

A Smart Trust Management Method to Detect On-Off Attacks in the Internet of Things

Internet of Things (IoT) resources cooperate with themselves for requesting and providing services. In heterogeneous and complex environments, those resources must trust each other. On-Off attacks threaten the IoT trust security through nodes performing good and bad behaviors randomly, to avoid being rated as a menace. Some countermeasures demand prior levels of trust knowledge and time to classify a node behavior. In some cases, a malfunctioning node can be mismatched as an attacker. In this paper, we introduce a smart trust management method, based on machine learning and an elastic slide window technique that automatically assesses the IoT resource trust, evaluating service provider attributes. In simulated and real-world data, this method was able to identify On-Off attackers and fault nodes with a precision up to 96% and low time consumption