3 research outputs found
Um repositório chave-valor com garantia de localidade de dados
Orientador : Prof. Dr. Carmem Satie HaraDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 09/08/2016Inclui referências : f. 67-76Resumo: Grandes volumes de dados produzidos diariamente trouxeram desafios envolvendo a definição de formas eficientes de como extraí-los, armazená-los e acessá-los. Entretanto, soluções tradicionais de bancos de dados não se mostraram eficientes diante de tais desafios, principalmente no requisito de escalabilidade. Uma possível abordagem para prover escalabilidade horizontal aos sistemas gerenciadores de banco de dados é a adoção de uma arquitetura estratificada, tendo como base um sistema de armazenamento distribuído com uma interface simples para o acesso a dados remotamente armazenados. Esta dissertação apresenta o ALOCS, um repositório distribuído de dados que adota o modelo chave-valor, mas que permite a alocação de um conjunto de pares agrupados em uma única estrutura, cuja localidade é controlada pela aplicação usuária do sistema. O controle de localidade permite que dados usualmente utilizados em conjunto possam ser alocados em um mesmo servidor, reduzindo a quantidade de comunicações entre servidores no processamento de suas consultas. Isto é essencial para prover escalabilidade e melhorar o desempenho de processamento das consultas em ambientes distribuídos. Os estudos experimentais mostram a melhoria no tempo de resposta das consultas utilizando a solução proposta.Abstract:Large volumes of data produced every day brought new challenges involving the definition of efficient ways to extract, store and access them. However, traditional database solutions are not efficient to solve these challenges, especially with respect to the scalability requirement. One approach to provide horizontal scalability to database management systems is the adoption of a layered architecture, based on a distributed storage system with a simple interface to access data remotely stored. This dissertation presents ALOCS, a distributed storage repository of data which adopts the key-value model, and which allows the allocation of a set of pairs grouped into a single structure whose location is controlled by the user application of the system. This control allows data commonly used together to be allocated on the same server, reducing the amount of communications between servers for query processing. This is essential to provide scalability and improve the processing of query execution in distributed environments. Experimental studies shows that ALOCS improves query response times by reducing the amount of remote data accesse
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A Cloud-Based Intelligent and Energy Efficient Malware Detection Framework. A Framework for Cloud-Based, Energy Efficient, and Reliable Malware Detection in Real-Time Based on Training SVM, Decision Tree, and Boosting using Specified Heuristics Anomalies of Portable Executable Files
The continuity in the financial and other related losses due to cyber-attacks prove the substantial growth of malware and their lethal proliferation techniques. Every successful malware attack highlights the weaknesses in the defence mechanisms responsible for securing the targeted computer or a network. The recent cyber-attacks reveal the presence of sophistication and intelligence in malware behaviour having the ability to conceal their code and operate within the system autonomously. The conventional detection mechanisms not only possess the scarcity in malware detection capabilities, they consume a large amount of resources while scanning for malicious entities in the system. Many recent reports have highlighted this issue along with the challenges faced by the alternate solutions and studies conducted in the same area. There is an unprecedented need of a resilient and autonomous solution that takes proactive approach against modern malware with stealth behaviour. This thesis proposes a multi-aspect solution comprising of an intelligent malware detection framework and an energy efficient hosting model. The malware detection framework is a combination of conventional and novel malware detection techniques. The proposed framework incorporates comprehensive feature heuristics of files generated by a bespoke static feature extraction tool. These comprehensive heuristics are used to train the machine learning algorithms; Support Vector Machine, Decision Tree, and Boosting to differentiate between clean and malicious files. Both these techniques; feature heuristics and machine learning are combined to form a two-factor detection mechanism. This thesis also presents a cloud-based energy efficient and scalable hosting model, which combines multiple infrastructure components of Amazon Web Services to host the malware detection framework. This hosting model presents a client-server architecture, where client is a lightweight service running on the host machine and server is based on the cloud. The proposed framework and the hosting model were evaluated individually and combined by specifically designed experiments using separate repositories of clean and malicious files. The experiments were designed to evaluate the malware detection capabilities and energy efficiency while operating within a system. The proposed malware detection framework and the hosting model showed significant improvement in malware detection while consuming quite low CPU resources during the operation