01. Universidade Federal Rural de Pernambuco - UFRPE (Sede)

URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/1

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Data inicial: 2020Assunto: search.filters.subject.Segurança de computadores

Resultados da Pesquisa

Agora exibindo 1 - 3 de 3
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    Utilização de inteligência de ameaças cibernéticas para prevenção e mitigação de ataques ransomware: uma revisão sistemática da literatura
    (2025-02-27) Cabral, Rennan Luis Barros; Lins, Fernando Antonio Aires; http://lattes.cnpq.br/2475965771605110; http://lattes.cnpq.br/4612932794474682
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    Uma análise do impacto das linguagens de programação nos custos de execução no AWS Lambda em cenários de cold start e warm start
    (2023-04-24) Andrade Júnior, Edilson Alves de; Medeiros, Robson Wagner Albuquerque de; http://lattes.cnpq.br/3169193612606500; http://lattes.cnpq.br/5131828050788518
    Public cloud computing solutions have gained visibility on the market for offering great advantages over on-premises systems. However, cloud-based management workflows also brings concerns. As well as problems related to information security and lack of skilled professionals, cost management is one of the main challenges faced by users and organizations that migrate or already have their operations on cloud. Cloud providers define variables that directly affect cost behaviors, in addition, factors such as key characteristics of programming languages can also contribute to change those behaviors. This work aimed to understand how programming languages behave in cloud services such as AWS Lambda, so that cost management is carried out more assertively and efficiently, directly contributing to the reduction of costs and financial waste when using this kind of service. The results showed that the characteristics of programming languages significantly interfere in the financial costs of execution, elucidating that the choice of a certain programming language should be considered when cost is a requirement to be met when using AWS Lambda.
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    Sistema para detecção de intrusão de botnets utilizando aplicações de machine learning
    (2021-12-13) Silva Neto, Francisco Queiroga da; Assad, Rodrigo Elia; http://lattes.cnpq.br/3791808485485116; http://lattes.cnpq.br/7900008638092251
    Communication tools and the continuous advancement of the Internet have also resulted in the sophistication of tools and methods to carry out attacks against users and their computers, with features that facilitate criminal activities in the cyber environment. Among cyber threats, botnets have characteristics and advantages that have expanded their use in recent years, becoming a tool employed extensively by attackers to conduct attacks and gain control of various devices connected to computer networks. The way these threats behave and are updated brings several challenges to the intrusion detection area. In this paper, a study is presented on the application of machine learning techniques in detecting botnets by analyzing network traffic flows. The study aims to show how pattern classification techniques can be applied in intrusion detection systems to identify similarities between the infrastructure of botnets, where works in the literature were studied to address an application that aims to improve the problems related to the attribute selection steps and the data processing, crucial steps in machine learning models.